电力系统故障识别分类与定位

A modern electric power system integrated with advanced technologies such as sensors and smart meters is referred to as a “smart grids”, aimed at enhancing electrical power delivery efficiency and reliability. However, fault location and prediction can become challenging when dynamic fault currents from renewable energy sources are present. To address these challenges, three unique deep learning models that make use of Deep Neural Networks (DNN) have been proposed. CNN, LSTM, and Hybrid CNN-LSTM are deep learning models. Line faulty identification (LF), fault classification (FC), and fault location estimate (FL) are the subjects on which they concentrate. These models analyze data gathered both pre and post faults occur in order to enhance decision making. Signals including the voltage and current were fed into these models from many different locations across the test networks. Once the 1D CNN has extracted characteristics from the gathered signals, LSTM uses these features to make accurate estimations and identify faults. Complex data are compatible with this method in terms of optimal outcomes. Using training and testing data from transmission line failure simulations, the proposed approaches were evaluated on the IEEE 6-bus and IEEE 9-bus systems. The tests encompassed a range of fault classes, locations, and ground fault resistances at various locations. Distributed Generator (DG) resources were additionally included in the system architecture and changes in the topology of the networks were considered in terms of location and number of DG resources. The results demonstrated that the proposed algorithms outperformed contemporary technologies in terms of detection, classification, and location accuracy. They demonstrated high accuracy and robustness in their performance.

… The fault identification production rule … power system fault diagnosis, we designed the case study of four cases. Moreover, to show the superiority of the CPVMs in the power system fault …

… changes in the microgrid under normal and fault conditions. The novelty of the proposed … of a fault in the microgrid. SVM-2 identifies the exact type and location of fault that occurred. The …

Unexpected failures in the electrical power transmission line can occur for several different, unpredictable reasons. Power failures on transmission lines can destroy the present power grid if faults aren't quickly detected and corrected. For consistent performance, it is essential to have a system in place for identifying and categorizing power system faults. Several academics have developed automated approaches for fault identification and classification; however, typical fault detection techniques depend on human feature extraction with previous understanding. It is crucial to detect transmission line faults to guarantee safety. Preventing costly damage to the network is one of the key advantages of earlier fault detection in a transmission line. Autonomous and efficient fault diagnosis in the power system remains a major problem in the area of intelligent fault diagnosis. Recent years have seen a surge in interest in the development of intelligent fault diagnosis techniques that make use of Machine Learning (ML). Different ML techniques for fault classification are presented in this research. Kaggle data is used after being cleaned and integrated. Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF) are the ML models used. Using the metrics of evaluation, the optimal model is found. Results from experiments demonstrate that the NB will outperform other methods for fault detection in power transmission lines, with an accuracy rate of 97.77%, recall of 97.09%, the precision of 98.64%, and Fl-score of 97.86%.

A fault identification scheme for protection and adaptive reclosing is proposed for a hybrid multi-terminal HVDC system to increase the reliability of fault isolation and reclosing. By analyzing the "zero passing" characteristic of current at the local end during the converter capacitor discharge stage, the fault identification scheme is proposed. The distributed parameter-based fault location equation, which incorporates fault distance and fault impedance, is developed with the injection signal and the distributed parameter model during the adaptive reclosing stage. The fault distance is determined using a trust region reflection algorithm to identify the permanent fault, and a fault identification scheme for adaptive reclosing is developed. Simulation results show that the proposed scheme is suitable for long-distance transmission lines with strong anti-fault impedance and anti-interference performance. Also, it is less affected by communication delay and DC boundary strength than existing methods.

… Power system operators commonly employ fault recorders to … of power sources and the sampling frequency of fault … of the fault waveform leading to fault identification error. This paper …

Modern power system protection schemes incorporate artificial intelligence (AI) techniques. However, in a conventional way, most of these schemes rely on the data of current and voltage collected from current transformer (CT) and potential transformer (PT) respectively. CTs suffer from the drawback of core saturation and impact the accuracy and effectiveness of intelligent methods. Also, it has the constraints of size, safety, and economy. The research here explores the effectiveness of magnetic sensors in advanced power system protection schemes as an alternative to traditional current sensing. In the presented work, a novel dataset is prepared by transforming transmission line currents into magnetic field components. Several supervised as well as unsupervised machine learning algorithms have been applied to this data instead of traditional currents and voltage for fault prediction. The paper discusses the comparative evaluation of these algorithms based on various performance metrices which reveals that Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN), random forest (RF), and extreme gradient boost (XGB) algorithms excel in fault detection, while multilayer perceptron (MLP) and KNN performs better fault classification. The findings promise the potential for developing compact, safe, and cost-effective protection schemes utilizing magnetic field sensors.

… Effective fault diagnosis is critical for ensuring power system … challenges in fault detection accuracy, limited fault type … ) for detecting and managing fault events on distribution systems, …

Ensuring the reliability of electrical distribution networks is a pressing concern, especially given the power outages due to surface contamination on insulating components. Surface …

… of faults during an emergency state of a power system, protection systems are required to have disturbance and fault state identification abilities. In this study, a novel fault diagnosis …

Traditional fault detection and location schemes become ineffective for detecting and locating the fault in dc microgrid systems due to integrating various types of power electronic-based dc loads and generators. To resolve this issue, advanced, intelligent, specialized fault detection and location schemes are necessary. This article suggests a novel fault detection scheme based on the difference in the teager energy available in the dc current wave at sending and receiving ends of lines. After the detection of the fault, the location is calculated by estimating the resistance of the cable up to the fault point as well as the total resistance of the cable, with the help of least square technique. The proposed scheme decides fault is internal if the estimated fault location is less than 1 p.u. Otherwise, the proposed scheme decides fault is external. A dc microgrid with different types of generating units and loads is simulated using MATLAB/Simulink to evaluate the developed algorithm. Internal and external faults, pole-to-ground faults, and pole-to-pole faults with changing fault resistance and fault location are some of the fault scenarios that have been simulated. The obtained simulation results prove that the suggested algorithm can discriminate between internal and external faults and locate the fault. The proposed technique is also examined on a dc microgrid hardware testbed and results show the efficiency of the suggested approach.

… and large-scale power outages. Hence, the rapid and accurate fault identification in key lines and weak links can help ensure the stability and safety of the power grid. However, existing …

Detecting and locating faults within electrical grids presents substantial challenges in power systems engineering, leading to energy loss, reduced revenue, and equipment damage. This paper provides a comprehensive review of the integration and utilization of machine learning algorithms to enhance fault identification processes. Acknowledging the constraints of traditional methods, the paper delves into the historical evolution of fault detection in power systems. By highlighting the significance of machine learning, this review underscores its pivotal role in fault prevention, energy conservation, and bolstering the resilience of power infrastructures.

Low-current grounding systems are the main grounding method used in power distribution networks and belong to non-direct grounding systems. The most common fault in this type of system is a single-phase grounding fault, which may cause electrical fires and endanger personal safety. Due to the difficulty of troubleshooting, the selection of fault lines in low-current grounding systems has always been an important research topic in power system relay protection. This study proposes a new approach for fault identification of power lines based on the Euler transformation and deep learning. Firstly, the current signals of the distribution network are rapidly Fourier-transformed to obtain their frequencies for constructing reference signals. Then, the current signals are combined with the reference signals and transformed into images using Euler transformation in the complex plane. The images are then classified using a residual network model. The convolutional neural network in the model can automatically extract fault feature vectors, thus achieving the identification of faulty lines. The simulation was conducted based on the existing model, and extensive data training and testing were performed. The experimental results show that this method has good stability, fast convergence speed, and high accuracy. This technology can effectively accomplish fault identification in power distribution networks.

… to fault events is of critical importance. This paper proposes a data-driven fault location identification and types classification … an accuracy of 91.4% for fault detection, 93.77% for correct …

Distributed energy generation increases the need for smart grid monitoring, protection, and control. Localization, classification, and fault detection are essential for addressing any problems immediately and resuming the smart grid as soon as possible. Simultaneously, the capacity to swiftly identify smart grid issues utilizing sensor data and easily accessible frequency and voltage data from PMU devices is a prerequisite of this task. Therefore, this paper proposes new methods using fuzzy logic and adaptive fuzzy neural networks as well as machine learning and meta-heuristic algorithms. First, line voltage is used by a fuzzy thresholding method to estimate when a transmission line defect would develop in less than 1.2 clock cycles. Next, features taken from frequency signals in the real-time interval are utilized to classify the type of error using machine learning systems (decision tree algorithm and random forest algorithm) optimized with wild horse meta-heuristic algorithm. To locate the precise problem location, we finally use a neural fuzzy inference system that is capable of adapting to new data. We employ a simulated power transmission system in MATLAB to test our proposed solutions. Mean square error (MSE) and confusion matrix are used to assess the efficiency of a classifier or detector. For the decision tree algorithm method, the detector attained an acceptable MSE of 2.34e−4 and accuracy of 98.1%, and for the random forest algorithm method, an acceptable MSE of 3.54e−6 and accuracy of 100%. Furthermore, the placement error was less than 153.6 m in any direction along the line.

… for fault detection, classification, and location estimation … fault identification tools are required. Costly consequences of probable faults motivate the need for immediate actions to detect …

Electric systems are getting more complex with time, and primitive protection methods such as traveling wave and impedance-based methods face limitations and shortcomings. This paper incorporates and presents the applications of an adaptive neuro-fuzzy inference system and compares it with a back propagation neural network, self-organizing map, and hybrid method of discrete wavelet with adaptive neuro-fuzzy inference system for fault detections, classification, and localization in transmission lines. These methods, in comparison with primitive methods, could be capable of detecting, identifying, and predicting the location of the faults more accurately. The IEEE 9-bus system is utilized to obtain data from one end of the transmission line to develop an ANFIS-based model. This system is simulated in MATLAB/Simulink for different fault cases at various locations. The three-phase voltage and current at one end of IEEE 9-bus number seven are taken for training. Three ANFIS models are developed for fault detection, classification, and localization and compared with other models. For verification of the models, mean square error, mean absolute error, and regression analysis have been computed and compared for all the models. All four techniques have performed well for fault classification, detection, and location. However, the percentage error for the ANFIS-based fault model is less compared to backpropagation, self-organizing map, and discrete wavelet transform with ANFIS. Therefore, the proposed ANFIS models can be implemented for deploying in real-time-based protection systems.

… reduced computational time for fault diagnosis of VSC–HVDC, we propose and intelligent fault detection methods from current signals to recognize automatically the DC faults in HVDC. …

Thanks to smart grids, more intelligent devices may now be integrated into the electric grid, which increases the robustness and resilience of the system. The integration of distributed energy resources is expected to require extensive use of communication systems as well as a variety of interconnected technologies for monitoring, protection, and control. The fault location and diagnosis are essential for the security and well-coordinated operation of these systems since there is also greater risk and different paths for a fault or contingency in the system. Considering smart distribution systems, microgrids, and smart automation substations, a full investigation of fault location in SGs over the distribution domain is still not enough, and this study proposes to analyze the fault location issues and common types of power failures in most of their physical components and communication infrastructure. In addition, we explore several fault location techniques in the smart grid’s distribution sector as well as fault location methods recommended to improve resilience, which will aid readers in choosing methods for their own research. Finally, conclusions are given after discussing the trends in fault location and detection techniques.

… faults limit the application of passive detection and protection technology, raising difficulties in DC line fault detection and location. … an active detection fault diagnosis and fault location …

Integrating distributed generation resources (DGRs) in the distribution system, forming microgrids or multi-microgrids (MMGs), has fulfilled the continuous rise in load demand over the past decade. However, the deeper infiltration of microgrids or MMGs in the distribution system provides an uninterruptable power supply and enhances the system's reliability. But their integration changes the system's fault current characteristics, radial nature, and fault current contribution levels. Thus, due to the abovementioned issues, the mal-operation of conventional relays may occur using the existing methods under fault conditions. Moreover, identifying low and high impedance faults is equally essential in active distribution networks (ADNs). Thus, this article proposes a novel fault detection index for identifying low and high impedance faults. Further, a fault classification index is also proposed for classifying low and high impedance faults. The energy associated with the differential value of the superimposed component of the positive sequence impedance is utilized for the proposed technique. It also accurately discriminates the internal and external faults, switching transients, and non-linear loading conditions. The efficacy of the proposed method is further evaluated in the presence of noise components, and by considering the communication delay from one end. A modified IEEE-13 bus distribution system consisting both single DGR unit and multi-microgrids are considered for simulating these test cases. The simulation is performed using Real Time Digital Simulator (RTDS). The experimental setup is used to validate the proposed method for real-time scenarios.

Accurate fault detection and localization play a pivotal role in the reliable and optimal operation of electric power distribution networks. However, the integration of intermittent distributed …

The accurate detection and identification of intermittent cable faults are helpful in improving the reliability of the distribution system. This paper proposes intermittent fault detection and identification for distribution networks based on machine-learning (ML) techniques. For this reason, the IEEE 33 bus system is simulated in the radial and mesh topologies by considering all possible single- and three-phase electrical faults and limitations to collect high-resolution voltage and current waveforms. Moreover, this simulation investigates and considers various cases including low-impedance faults (LIFs) and high-impedance faults (HIFs) with a short and long duration. The collected data from the simulation are used for high-impedance intermittent fault detection, classification, and branch identification using eight supervised learning methods. A comparison between the accuracy and error of these ML classifiers shows that gradient booster (GB) and K-nearest neighbors (KNN) have the best performance for all three objectives. However, GB has a very high computation time compared to KNN.

Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics and discern fault categories from the three-phase raw of voltage and current signals. With the rise of distributed generators, conventional relaying devices face challenges in managing dynamic fault currents. Various deep neural network algorithms have been proposed for fault detection, classification, and location. This study introduces innovative fault detection methods using Artificial Neural Networks (ANNs) and one-dimension Convolution Neural Networks (1D-CNNs). Leveraging sensor data such as voltage and current measurements, our approach outperforms contemporary methods in terms of accuracy and efficiency. Results in the IEEE 6-bus system showcase impressive accuracy rates: 99.99%, 99.98% for identifying faulty lines, 99.75%, 99.99% for fault classification, and 98.25%, 96.85% for fault location for ANN and 1D-CNN, respectively. Deep learning emerges as a promising tool for enhancing fault detection and classification within smart grids, offering significant performance improvements.

Hybrid microgrids are emerging technology that integrates the key characteristics of AC and DC microgrids without requiring significant changes to the distribution network. However, its protection faces a lot of challenges due to the integration of various types of Distributed Generation (DG). Integration of DGs into the power system causes a change in the magnitude and directions of short circuit current. Hence the design of the overcurrent protection in a microgrid becomes more complex. This drawback of the conventional protection method is solved by using intelligent protection schemes. In this work, the artificial neural network (ANN) is used for fault detection, classification, and locating faults in hybrid microgrids which are operated in islanded mode. This paper used MATLAB software for generating the dataset of fault voltages and currents from the test model. The trained ANN models were tested for 11 types of faults and nofault scenarios. Mean Square Error (MSE), regression plots, and receiver operating characteristics (ROC) were used to evaluate the performance of the neural network models. The results are encouraging.

Fault detection and localization in electrical power lines has long been a crucial challenge for electrical engineers as it allows the detected fault to be isolated and recovered promptly. These faults, if neglected, can rupture the normal operation of the network and drastically damage the power lines and the equipment attached to it. The wastage of power and money due to these faults can be harmful to the economy of an industry or even a country. Therefore, efficient fault detection mechanisms have become crucial for the well-being of this power-hungry world. This research presents an end-to-end deep learning strategy to detect and localize symmetrical and unsymmetrical faults as well as high-impedance faults (HIFs) in a distribution system. This research proposes a novel deep convolutional neural network (CNN) transformer model to automatically detect the type and phase of the fault as well as the location of the fault. The proposed model utilizes 1-D deep CNNs for feature extraction and transformer encoder for sequence learning. The transformer encoder utilizes an attention mechanism to integrate the sequence embeddings and focus on significant time steps to learn long-term dependence to extract the context of the temporal current data. The different faults were simulated in MATLAB Simulink using IEEE 14-bus distribution system. The proposed models were found to produce better performance on the test database when evaluated using F1-Score, Matthews correlation coefficient (MCC), and accuracy. The models also produced better predictions on HIFs compared to conventional fault-detection techniques.

This paper proposes a new algorithm for the detection and classification of faults in transmission lines in the presence of distributed generation. The proposed algorithm uses wavelet transform-based detail and approximation coefficients of voltage and current signals over one cycle after fault inception at one end of the transmission line. The line impedance calculated from the ratio of the voltage and current approximation coefficients is fed to the fuzzy inference system to classify faults. The proposed algorithm has been tested successfully in a real-time digital simulator for nonlinear high impedance faults in the presence of wind farm, considering the effects of CT saturation. All types of faults with the variation of pre-fault loading condition, line parameters, line length and source impedance can be correctly classified using the proposed algorithm.

This article proposes a deep learning (DL) model made of Long Short Term Memory (LSTM) and Adaptive Neuro Fuzzy Inference System (ANFIS) to detect fault in smart distribution grid assisted by communication systems using smart meter data. In smart grid, data analysis for fault identification and detection is crucial for grid monitoring. Nowadays, there are several DL techniques developed for smart grid data analysis applications. To solve this problem, a novel data analysis model based on deep learning and Neuro‐fuzzy algorithm is developed for fault location in a smart power grid. First, the LSTM is applied for training the data samples extracted from the smart meters. Then, an ANFIS algorithm is implemented for fault detection and identification from the trained data. Finally, faults are located with the higher accuracy. With this intelligent method proposed, single‐phase, two‐phase and three‐phase faults can be identified using a restricted amount of data. The novelty of the proposed method compared with other methods is the capability of fast training and testing even with large amount of data. To verify the effectiveness of our methodology, an intelligent model of the IEEE 13‐node network is used. The effectiveness and robustness of the proposed model are evaluated using several parameters such as accuracy, precision‐recall, F1‐score, Receiver Operating Characteristic (ROC) curve and complexity time. The obtained results indicate that the proposed deep learning model outperforms existing deep learning methods in the literature for fault detection and classification with 99.99% accuracy.

… The relaying scheme proposed for fault detection and classification in the transmission system consists of three parts: input preparation, fault detection, and fault classification. The …

… learning optimizes PMU placement, … fault classification efficiency using PMU data. Finally, pre-trained models paired with k -means clustering augment fault data, boosting classification …

Machine learning (ML) algorithms are increasingly used in power systems applications. One important application is the classification and localization of various types of transmission line faults. Using voltage and current measurements from phasor measurement units (PMUs), a number of useful features can be extracted, which can form the basis of a ML-based prediction of the fault type, line, and distance on the line. This paper proposes a technique to find the optimal number and placement of PMUs by performing thorough feature selection. The features are selected to maximize the accuracy of the ML classification and regression algorithms. The results show that for the IEEE 14 bus system, the use of only five PMUs is sufficient to obtain high levels of accuracy. For example, a testing accuracy of 99.0% and 97.1% can be achieved for the fault type and fault line location, respectively. As for the fault distance along the line, the testing MAE of 3.1% can be obtained along with an R2 score of 94.4%. Adding more PMUs does not provide any additional value in terms of accuracy.

Knowing the exact location of a short-circuit fault in a power distribution system (PDS) is essential for rapid restoration of service to customers and has a direct impact on the operational costs and reliability of the system. In this paper a phasor measurement unit (PMU) based method for fault localization and classification is presented. By introducing the concept of a virtual bus, the exact fault location on the line is determined rather than just a bus closest to the fault. Moreover, after determining the fault location, a generic fault model (GFM) is introduced to classify the type of fault by solving a minimization problem, which also provides fault impedances as a result. These can then be further used to determine if the fault occurred on the lateral instead of the main feeder without using additional PMU at the end of that lateral. The effectiveness of the proposed approach is verified by simulating various fault scenarios in two test systems based on real PDSs using a real-time digital simulator. In addition, a sensitivity analysis of the method is performed for different noise levels in the input parameters to verify its applicability to the real system.

… This section shows how several studies have been conducted throughout the years to classify faults in smart grids. This paper reviews and provides an overview of current …

… an HHHNN-BOA approach for fault classification. First, PMU data are pre-processed using … and relational dependencies for accurate fault classification. The BOA is employed during …

… While the current work demonstrates the use of virtual PMU, IoT, and fault classification using RBFELM, several avenues exist for future investigation to enhance the robustness and …

An effective control system is required to increase the power system’s safety, effectiveness, and reliability. Phasor measurement unit (PMU) and the wide area measurement systems (WAMS) both synchronized with the global position system can provide timestamp and synchronized phasors which provides a dynamic view of power system. Combining these synchronized measurements in a central protection system (CPS), a wide area monitoring system is created by means of optical fiber communication. Since PMU devices are more efficient than standard SCADA (Supervisory Control and Data Acquisition) systems at capturing the rapid dynamics of the power system, this technology is becoming popular in the utility industry. The suggested concept is designed to operate promptly for protecting large power transmission networks using PMUs. The proposed technique has been implemented on IEEE 5-bus network with MATLAB 2021a and has identified faults within a few milliseconds. The machine learning technique was used to identify anomalies, aiming to enhance the reliability of the power system. To analyze the massive amounts of PMU data produced in the power systems, this study has provided classification methods for K Nearest Neighbors, Logistic Regression method and Support Vector Classifier. These algorithms’ results indicate that they are quite accurate at identifying anomalies in the data provided by PMUs.

Fault location is one of the main challenges in the distribution network due to its expanse and complexity. Today, with the advent of phasor measurement units (PMU), various techniques for fault location using these devices have been proposed. In this research, distribution network fault location is defined as an optimization problem, and the network fault location is determined by solving it. This is done by combining PMU data before and after the fault with the power system status estimation (PSSE) problem. Two new objective functions are designed to identify the faulty section and fault location based on calculating the voltage difference between the two ends of the grid lines. In the proposed algorithm, the purpose of combining the PMU in the PSSE problem is to estimate the voltage and current quantities at the branch point and the total network nodes after the fault occurs. Branch point quantities are calculated using the PMU and the governing equations of the π line model for each network section, and the faulty section is identified based on a comparison of the resulting values. The advantages of the proposed algorithm include simplicity, step-by-step implementation, efficiency in conditions of different branch specifications, application for various types of faults including short-circuit and series, and its optimal accuracy compared to other methods. Finally, the proposed algorithm has been implemented on the IEEE 123-node distribution feeder and its performance has been evaluated for changes in various factors including fault resistance, type of fault, angle of occurrence of a fault, uncertainty in loading states, and PMU measurement error. The results show the appropriate accuracy of the proposed algorithm showing that it was able to determine the location of the fault with a maximum error of 1.21% at a maximum time of 23.87 s.

This paper presents a rules-based integrated fault detection, classification and section identification (I-FDCSI) method for real distribution networks (DN) using micro-phasor measurement units (μPMUs). The proposed method utilizes the high-resolution synchronized realistic measurements from the strategically installed μPMUs to detect and classify different types of faults and identify the faulty section of the distribution network. The I-FDCSI method is based on a set of rules developed using expert knowledge and statistical analysis of the generated realistic measurements. The algorithms mainly use line currents per phase reported by the different μPMUs to calculate the minimum and maximum short circuit current ratios. The algorithms were then fine-tuned with all the possible types and classes of fault simulations at all possible sections of the network with different fault parameter values. The proposed I-FDCSI method addresses the inherent challenges of DN by leveraging the high-precision measurements provided by μPMUs to accurately detect, classify, and sectionalise faults. To ensure the applicability of the developed IFDCSI method, it is further tested and validated with all the possible real-time events on a real distribution network and its performance has been compared with the conventional fault detection, classification and section identification methods. The results demonstrate that the I-FDCSI method has a higher accuracy and faster response time compared to the conventional methods and facilitates faster service restoration, thus improving the reliability and resiliency indices of DN.

Modern power grids rely heavily on timely and accurate fault detection to ensure stable and reliable operation. This paper proposes a data-driven framework that leverages deep learning to detect and classify multiple types of faults in transmission networks using high-resolution phasor measurement unit (PMU) data. Our approach employs an LSTM-based autoencoder trained on normal operating conditions to learn typical voltage and power flow patterns. Deviations from these learned patterns are identified as anomalies, and latent-space clustering is used to differentiate fault types such as forced oscillations, bus trips, and generator trips. The method is designed to integrate seamlessly with grid control systems, providing near real-time insights to operators. This work contributes to the advancement of information processing methods in power system management by combining temporal sequence modeling with unsupervised classification techniques, aiming to enhance fault diagnosis capabilities in complex technical systems.

… The proposed method exhibits superior discriminative power and enhanced robustness in feature fusion and fault classification compared to conventional single‑modality approaches. …

This paper presents a real-time hardware-in-the-loop (HIL) testbed designed for fault detection, classification, and location (FDCL) in power distribution systems, utilizing Distribution Phasor Measurement Units (D-PMUs) and the OPAL-RT simulator. The testbed integrates D-PMUs and the OPAL-RT platform to generate time-synchronized measurements at strategic locations within a medium voltage (MV) grid. The FDCL algorithm leverages these measurements along with a digital twin model of the grid to enhance fault management capabilities. The algorithm uses the D-PMUs data to accurately identify faulted areas and estimate fault locations, regardless of fault type, grid topology, or grounding system. A series of test cases, involving various fault types, resistances, and locations, were conducted to validate the performance of the FDCL system. Results demonstrate the algorithm's efficacy in quickly and accurately identifying and locating faults, thus minimizing outage durations and improving grid reliability.

This paper makes a contribution to the field of fault location finding in a new way that helps in the improvement of grid reliability. This paper proposes a study-based approach for fault allocation and fault type classification that uses the study of voltage and current frequency during the abnormal condition. Although, ideally frequency of voltage and current are the same in the abnormal condition they may differ from each other. This difference in frequency is separately measured by the phasor measurement unit (PMU) block at MATLAB/Simulink platform. The PMU (PLL-based, positive-sequence) block is inspired by the IEEE Std C37.118.1-2011. In this approach, we measure the line voltage and current frequency variation with the help of installed PMU after this we present this measurement in characteristics form with the help of the scoping tool in MATLAB/Simulink and study them one by one, and proposed a conclusion for fault location identification and fault type classification. The proposed approach is able to identify the source side and load side fault location and also able to classify faults into two categories namely symmetrical and asymmetrical. The proposed approach is tested on two MATLAB/Simulink models and observed satisfactory.

Grid management undoubtedly benefits from an accurate monitoring of actual network operating conditions. Such monitoring can be obtained starting from a widespread presence of measurement points and different types of estimation techniques. To exploit all the possible measurements present on the network, data coming from the protection systems can be considered in addition to those from the measurement systems. In this scenario, however, accuracy should not be underestimated, because in the presence of dynamic signals it can be dramatically reduced. In this context, this paper presents an improved fault detection and location method based on synchronized measurements, estimation techniques and an appropriate accuracy modeling aimed at reducing the uncertainty in the fault analysis. The proposed approach is validated by simulations carried out by means of a Digital Real Time Simulator (DRTS) on the three-phase CIGRE European Medium Voltage distribution network.

An overview of the many methods used for fault detection, classification and location in the power system, particularly in transmission lines, is provided in this review, it also includes an experimental result of adaptive neuro-fuzzy inference system -based fault detection , fault classification and fault location. Being in operation outdoor environment, transmission lines are more vulnerable to various faults which may lead to system collapse in severe cases. Therefore, to ensure the reliable and safe operation of power system it is imperative to critically monitor the faults in transmission lines. In this regard, researchers around the globe have developed several techniques and constantly putting efforts to further improve the protection efficacy. The brief yet thorough analysis and comparison of the artificial intelligence-based techniques, hybrid methodologies and most recent approaches in the context of power system faults have been discussed and presented. In addition, the research work and the experimental results of an adaptive neuro-fuzzy inference system-based techniques have also been discussed for IEEE-9 bus system. The mean square error for testing data of ANFIS-based fault detection, classification, is zero and for fault location Mean square error is 5.32km. This piece of work could be helpful in the development of a comprehensive understanding of various artificial intelligence-based techniques within the realm of fault detection, classification and localization in transmission lines.

Fault location estimation in transmission lines is critical for power system reliability. Various methods have been developed for this purpose, among which transient frequency spectrum analysis (TFSA) stands out as a recent method based on travelling wave (TW) theory. TFSA determines the fault location by analyzing the frequency spectrum of transient currents and/or voltages at the instant of the fault, offering advantages such as independence from fault impedance and the ability to locate faults with one-side measurements. Despite its success in fault location, TFSA has several considerations that warrant detailed investigation. This study explores the effects of source inductance, series compensation, fault arc, and current transformer (CT) characteristics on transient frequencies. Additionally, the impact of noise on TFSA results is examined. The new proposed source inductance compensation method can reduce the error of 6.55% to 0.88%, where the same error can be reduced to 3.45% with the compensation method given in previous study. Strategies to enhance accuracy are discussed and compared to previous studies, including a proposed detection approach providing appropriate data size and precise wave propagation speed calculations. These findings contribute to a deeper understanding of TFSA’s limitations and inform practical improvements for fault location accuracy in power transmission systems.

: A balanced operating power system with all elements carrying normal currents and bus voltages within the prescribed limits can be disrupted due to faults within the system. Overhead transmission networks are vulnerable to the vagaries of the atmosphere and, therefore, statistically have the highest probability of fault occurrence. Quick and accurate fault detections assist in timely remedial action, offering significant economic and operational benefits. Maintaining continuous and uninterrupted supply functionality is one of the critical objectives of electric utilities for a reliable system operation. Also, identifying and locating faults is crucial to address them in time to avert the risk of cascading failures. During faults, fast electromagnetic transients associated with the current and voltage waveforms can provide valuable insights into identifying abnormal operating conditions. To analyze these non-stationary signals in both the time and frequency domains, wavelet transform (WT) has become an indispensable tool. Thanks to its ability to adapt to variable window sizes, WT provides a more accurate and detailed resolution, making it a highly useful technique for signal analysis. In this context, this paper presents the application of WT-based intelligent technique to detect and classify power system faults accurately. The transient disturbances caused by various faults are subjected to wavelet transform analysis to analyze the detail coefficients of phase currents. The maximum detail coefficients of phase currents, which differ significantly when the system experiences a fault, served as the distinguishing feature to identify different power system faults. The phase current signals are analyzed with one of the wavelets from the Daubechies 4 (db4) family to obtain detail coefficients, thus enabling the categorization of the faults. Extensive simulation tests for fault types have been conducted on the standard IEEE 5-Bus system to demonstrate the technique’s effectiveness and fault detection capability, allowing utilities to take timely protective actions.

The requirements for the increased penetration of renewable energy sources in electrical power systems have led to a dominance of power electronic interfaces. As a result, short-circuit currents have been reduced by the thermal limitations of power electronics, leading to problems associated with the sensitivity, selectivity, and reliability of protective relays. Although many solutions can be found in the literature, these depend on communications and are not reliable in all grid topologies or under different types of electrical fault. Hence, in this paper, the analysis of ground fault currents and voltages using a wavelet transform in combination with a new algorithm not only detects such ground faults but also allows them to be cleared quickly and selectively in scenarios with low fault current contribution due to a full penetration converter-interface-based generation. To verify and validate the proposed protection system, different ground faults are simulated using an arc ground fault model in a grid scheme based on the IEEE nine-bus standard test system, with only grid-forming power converters as generation sources. The test system is modelled in the MATLAB/Simulink environment. Therefore, the protection relays that verify all the steps established in the new algorithm can detect and clear any ground defect. Simulations are also presented involving different fault locations to demonstrate the effectiveness of the proposed ground fault protection method.

: The integration of a high proportion of renewable energy introduces significant challenges for the adaptability of traditional fault nature identification methods. To address these challenges, this paper presents a novel fault nature identification method for renewable energy grid-connected interconnection lines, leveraging wavelet packet decomposition and voltage waveform time-frequency morphology comparison algorithms. First, the paper investigates the harmonic injection mechanism during non-full-phase operation following fault isolation in photovoltaic renewable energy systems, and examines the voltage characteristics of faulted phases in renewable energy scenarios. The analysis reveals that substantial differences exist in both the time and frequency domains of phase voltages before and after the extinction of transient faults, whereas permanent faults do not exhibit such variations. Building on this observation, the paper proposes a voltage time-frequency feature extraction method based on wavelet packet decomposition, wherein low-frequency waveform components are selected to characterize fault features. Subsequently, a fault nature identification method is introduced, based on a voltage waveform time-frequency morphology comparison. By employing a windowing technique to quantify waveform differences before and after arc extinction, this method effectively distinguishes between permanent and transient faults and accurately determines the arc extinction time. Finally, a 220 kV renewable energy grid connection line model is developed using PSCAD for verification. The results demonstrate that the proposed method is highly adaptable across various fault locations, transition resistances, and renewable energy control strategies, and can reliably identify fault nature in renewable energy grid connection scenarios.

Precise fault location of distribution network plays an important role in the reliability of modern power systems. The integration of a large number of power electronic devices into the active distribution network results in limited fault currents, indistinct fault characteristics, and complex harmonics, which significantly impair the accuracy of the phasor-domain fault section location methods. In this study, we employ transient measurements to address the issue of single-phase ground fault location in various complex operational scenarios. By using the fault information from the transient zero-sequence current, a fault location method is proposed based on the discrete Fréchet distance of the transient zero-sequence current amplitude. This method utilizes Hilbert transform to extract the instantaneous amplitude of the transient zero-sequence current for each section of the line. According to the principle that the magnitude difference of the transient zero-sequence current at both ends of the fault line is the largest, a location criterion is established to accurately identify the fault section of single-phase ground faults in complex operational scenarios within the distribution network. The proposed fault location method have the ability to adapt to transient processes in active distribution network. The simulation system is constructed using Simulink to verify the accuracy of the section positioning algorithm under diverse conditions. And the results show that the method can be applicable to various neutral grounding modes and scenarios of distributed generation (DG) grid connection.

To refine the accuracy of fault location in the case of single-phase ground faults in distribution networks, this research delves into the ephemeral part of the fault signal that arises from these faults, and forms a fault routing scheme based on the transient energy method and the great value detection method of wavelet transform. To enhance the distinction between the transient signatures of a faulted line and a healthy line in the aftermath of a ground fault, this study applies wavelet transform to dissect and then reassemble the zero-sequence current signals across every line, captures the high-frequency wavelet coefficients, and subsequently measures the energy across each high-frequency range and the eigenfrequency range, pinpointing the maximum energy and modal peak for every line. The detection of faulty lines is achieved by comparing the maximum energy of every line with the modal maximum to differentiate between faulty and sound lines.

… other wavelet transforms deliver an appropriate level of accuracy by employing real-world travelling wave data in a proposed wavelet-based fault locator. … a wavelet-based fault location …

This paper mainly studies a fault identification and location technology for optical cable lines using transient traveling wave mode maxima method, which mainly includes the following steps: real-time monitoring and synchronous upload of traffic information; Information collection and preservation; Cable fault identification, including: current oscillogram drawing, fractal box dimension calculation, current information space transformation, discrete wavelet transform and wavelet coefficient calculation for flow information in mode space, inspection of maximum point of initial traveling wave mode of flow information, and cable fault identification; Cable fault location, including: drawing of current oscillogram, spatial transformation of current information β The module voltage component and the module voltage component of the flow information on the load side are calculated using discrete wavelet transform and wavelet coefficients. The maximum point of the initial traveling wave mode of the flow information is checked, and the cable fault is located. The technical process of this paper is simple, the calculation method is simple, the accident identification and location efficiency is high, the accuracy is good, the technology is comprehensive, and the application is strong.

… In this paper, a hybrid transmission line fault localization method combining wavelet packet … , the fault location methods proposed in this paper exhibit lower errors than the wavelet …

The reliable operation of modern power systems is critically dependent on the rapid and accurate isolation of transmission line faults, as failures can trigger cascading outages with severe socioeconomic consequences. While conventional protection schemes like overcurrent and distance relays are widely deployed, they exhibit limitations in speed, selectivity, and performance under high-impedance or evolving fault conditions, representing a significant gap in ensuring grid resilience. To address this, the objective of this research is to design and validate a novel Wavelet Transform Analysis with traditional relaying to enhance fault detection and classification. Through comprehensive modeling and simulation in MATLAB/Simulink, the proposed system demonstrated a mean fault detection time of 11.4 milliseconds and an accuracy of 99.8%, significantly outperforming conventional methods, particularly in challenging scenarios such as high-impedance and intermittent faults. These findings imply that the wavelet-enhanced framework offers a robust, adaptive solution for modern and future power networks, contributing directly to improved system stability, reduced outage times, and a foundational step toward intelligent, self-securing grid infrastructure.

The purpose of the work is to substantiate the possibility traveling wave fault location (TWFL) complex functioning in branched distribution networks of medium voltage class. Traveling waves (TW) are born in the place of fault. The complex registers the time of TW arrival to the ends of the network in a unified satellite time scale. Then the complex calculates the fault location. The main factor complicating the complex's functioning in branched networks of medium voltage class lines is the presence of a large number of concentrated inhomogeneities in the form of nodes with branches. Analytically and with the help of model calculations in PSCAD program, we can obtain TW transmission coefficient through inhomogeneity nodes at different types of faults. The paper shows the experiments' results. A large number of complex's sensors in several ends of the branched network allows us to determine the time TW registration and the speed of their propagation along the lines between paired combinations complex's sensors. Comparison of the registered propagation speed with the speed of light allows us to separate reliable or accurate registrations of the TW beginning from unreliable registrations. It is shown, that the amplitude of the registered TW and the amplitude of stationary and non-stationary noise at the place of registration determine the reliability of the complex operation. The paper compares the results of TW registration generated inside and outside the area controlled by the complex.

Aiming at the problem of insufficient fault location accuracy within the Feeder Terminal Unit (FTU)-monitored section of distribution networks, a double-ended traveling wave location method integrating modulus transformation and wavelet analysis is proposed. The proposed method first employs Clark transformation to decouple three-phase voltage and current signals to suppress inter-phase coupling. Then, an adaptive convolution algorithm based on the Mexican Hat wavelet is designed to accurately extract the traveling wave head features. Finally, the fault distance is calculated using the double-ended time difference method. Through the synergistic optimization of modulus transformation and wavelet analysis, the simultaneous improvement of traveling wave feature extraction accuracy and anti-interference capability is achieved. Simulation results demonstrate that the proposed method, with its progressive processing of signal decoupling, feature extraction, and distance calculation, effectively enhances the accuracy and reliability of fault location within FTU-monitored sections.

… In this paper a wavelet based power spectral density (PSD) has been utilized to detect fault, identify faulty phases and locate fault in the microgrid. Wavelet based PSD decouples the …

… fault location in distribution power systems is presented. Using computational simulations, travelling waves theory, wavelet … tries to approximate the fault location using data provided by …

Travelling Wave Fault Location becomes more and more popular. Users worldwide praise the great accuracy of travelling wave fault location. Due to the principle of travelling wave fault location the accuracy of the fault location is based on an accurate time measurement of the travelling wave wavefronts. For travelling wave fault location, the time accuracy should be in the range of nanoseconds because a time inaccuracy of one microsecond would cause an error of 300 m for the fault location. For double ended travelling wave fault location this time accuracy needs to be maintained for both devices which can be placed several hundreds of kilometres away from each other. Before putting such travelling wave fault location systems into operation different tests should be performed to guarantee the performance of the system. Users should start with a factory acceptance test to prove the accuracy of the system in a lab environment. In the factory acceptance test many test cases should be applied to test the accuracy of the system for different fault types and fault positions on the line. The factory acceptance test should be performed with the exact propagation velocity of the line. The inaccuracy of the fault location in factory acceptance test should be independent of the fault position otherwise there could be a problem with the propagation velocity. The accuracy of fault location during the factory acceptance test should be constant over time to demonstrate the reliability of time synchronization. If the factory acceptance test is passed it is confirmed that the fault location system itself can fulfil the accuracy requirements without the influence of the primary system like instrument transformer and the wiring between instrument transformers and travelling wave devices. During the site acceptance test a focus should be given to the accuracy of the time synchronization of the travelling wave device on site. Beside this, it needs to be checked that all channels are wired correctly, and the trigger levels are appropriate. For enhanced accuracy the compensation for the propagation time between instrument transformer and travelling wave device needs to be tested. At site acceptance test switching operations of primary equipment can be used to check the proper behaviour of the travelling wave devices. This can be helpful to adjust trigger levels and check the propagation velocity of the line and the time synchronisation of the travelling wave devices at both ends of a line. Finally, the communication of the travelling wave devices to the central computer needs to be tested at the site acceptance test. The paper starts with a short introduction of travelling wave fault location, followed by a discussion of possible sources of inaccuracy for single ended and double ended travelling wave fault location. Factory acceptance test and site acceptance test are explained in detail using practical examples. The paper closes explaining how to verify the settings, especially the propagation velocity of the line. This can be done after putting the system into operation, using data from the first external faults.

--Electrical systems have been facing transformations, such as distributed generation insertion, system expansion and regulatory standards in order to increase reliability and quality of the power supply. Thus, fault location methods must be updated to ensure accuracy in estimating the location of electrical faults. The delay in restoring the system causes damage to utilities and consumers. Considering this, the current work presents an approach capable of locating faults accurately in radial distribution systems. At first, the distance is estimated using the travelling wave theory with data from measurements from two terminals. Next, due to the radial characteristic of the system, the proposal aims to mitigate the problem of multiple estimation of faults. Thus, features are extracted from the voltage and current signals, which are used as inputs of decision trees to identify the fault region. The proposed approach was validated in a medium voltage distribution system, in which the results presented an average error of 0.79% (with a standard deviation of 0.4%) in estimating the fault distances and an average accuracy above 88.7% in identifying the region under fault. Thus, it was demonstrated that the proposed methodology is efficient to locate faults, mitigating the problem of multiple estimation.

Travelling wave based fault location methods are promising to accurately determine fault location in transmission lines. The velocity of travelling wave could be inaccurate, due to various operating conditions, weather conditions and transmission line types. The errors of travelling wave velocity will propagate to the fault location results. To overcome this challenge, this paper proposes a velocity-free single-ended fault location method based on travelling waves, by fully investigating the redundant information within the subsequent arrival times of travelling waves. First, with different system and fault conditions, the possible paths of travelling waves are classified into 9 different scenarios. Next, with the polarities and arrival times of subsequent wavefronts, the fault scenario is identified and the fault location is determined. Simulation results show that, with local end instantaneous measurements, the proposed method demonstrates robustness against wave velocity errors compared to existing single-ended and double-ended travelling waved based approaches. The proposed method works for both overhead transmission lines and underground cables.

… fault section. The approach does not need to install recorders at all terminals, and has high fault location … multi-branch hybrid transmission lines, fault locating methods based on pattern …

Some existing fault location methods for distribution networks rely too much on the local wave head information of the time or frequency domain signals, making it difficult to adapt to the increasingly complex structure and operating conditions of the distribution network after the new energy access. For improvement, the travelling wave (TW) time and frequency ranges that can effectively avoid waveform distortion caused by new energy access are analysed. The one‐to‐one matching relationship between the TW waveforms in these ranges and the fault positions is revealed. A fault TW time–frequency matrix with specific time and frequency windows is constructed, and a new distribution network fault location method is proposed based on the matching technique of waveform features, which realises the accurate fault location by exploring the proportionality between the cumulative trend of the matrix energy amplitude deviation and the fault point position. Simulation test results show that the proposed method is not affected by the complex structure of distribution networks such as new energy access and overhead‐cable line mixing on fault location and flexibly transforms the fault location problem into a time–frequency TW waveform matching problem, which improves the accuracy and robustness of the fault location for new distribution networks to a degree.

The topology of the distribution network and direction of the power flow will change when distributed generators (DGs) are connected to it, making it difficult to locate faults using conventional techniques like the impedance approach. Aiming at the two-phase short-circuit grounding faults of active distribution networks, this paper proposes a fault location method based on the time difference of the traveling wave modulus. The first step is the proposal of a zero-mode time-of-arrival calibration method for the ideal frequency band through the analysis of the attenuation of zero-mode traveling wave transmission. Next, define the relative wave velocity, research the quantitative relationship between the modulus transmission time difference and the zero-mode and aerial-mode wave velocities, and establish equation constraints between the modulus transmission time difference, relative wave velocity, and transmission distance. Then, time bounds and dynamic inequality constraints that establish relative wave velocities by fitting. Finally, combined with the abnormal data processing strategy, with the goal of minimizing the weighted deviation of the modulus time difference, the particle swarm optimization (PSO) algorithm is used to solve the fault distance. The PSCAD simulation result demonstrates that the method proposed in this paper has the advantages of high accuracy, strong error tolerance, and strong adaptability, and can quickly and accurately locate faults.

This paper reviews traveling wave based fault localization methods, including the traditional single-ended and the double-ended methods, and a most recent one. The first method relies on local measurements only while the second one relies on measurements from both ends. Both methods require the information of line length and traveling wave propagation speed. To determine the speed, an additional experiment is required to generate traveling waves. The third method is based on the fault location computing equations from the first two methods. Further manipulation leads to a new computing equation without the input of propagation speed. The paper also presents how to extract traveling waves from instantaneous measurements and how to compute fault location using computer simulation. A 300-km transmission line energized by two 500 kV, 60 Hz sources is simulated in OPAL RT-LAB real-time digital simulator. Traveling waves are extracted from the phase currents by use of a high-pass filter with kilo-Hz bandwidth. The three methods are tested and demonstrated for various fault types at different locations. In addition, characteristics of traveling waves of different fault types have been closely examined.

… effectiveness of fault locator. In this paper, an adjacent fault-free line based fault location scheme for … Since the traveling wave from adjacent fault-free line results in severe difficulty for …

… Although the travelling wave (TW) fault location approach is … Therefore, a novel TW fault location approach for LCC-MMC-… Secondly, the novel TW fault location approach and its …

A method is proposed for locating fault on two-terminal transmission lines using traveling-waves from two-ended unsynchronized current measurements. It considers the line length to be divided into four zones and computes per unit fault locations assuming the fault to be present in each of these zones separately. It then estimates the faulted zone, and the corresponding fault location estimate is displayed as the final output. These computations involve measuring three travelling wave arrivals at each terminal. The method is free of wave speed (or line parameter) input and is inherently immune to data synchronization issues. The method can work satisfactorily even when current measurements from both the ends are not properly synchronized. It is tested on a 200 km transmission line simulated in PSCAD/EMTDC. It works satisfactorily for various fault scenarios and is robust to errors in wave speed and data synchronization errors. The performance is also compared with a classical two–terminal method.

This letter presents a practical methodology to improve traveling wave (TW)-based fault location (TWFL) on transmission lines by leveraging classical single- and double-ended TWFL functions available in off-the-shelf relays and digital fault recorders. It eliminates negative effects of uncertainties in line propagation velocity and travel time, being free of time-consuming or complicated procedures. Real TW records are studied to demonstrate that the proposed solution can improve the accuracy and reliability of TWFL estimations when uncertainties about line propagation velocity or travel time exist.

To address the issues of location accuracy in traveling wave (TW) network location methods, which are susceptible to clock synchronization errors and TW speed inaccuracies. We propose a TW network location method based on virtual fault time difference information, simulating the propagation of TWs using virtual faults (VFs). Theoretically, the study analyzes the impact of transition resistance and TW heads (TWHs) aliasing on TWHs calibration, proposing an optimal effective time difference criterion that is unaffected by these factors. Algorithmically, VFs are first set in the faulty line, and a path search algorithm is used to obtain all TWH paths from the VF point to the measurement points. Subsequently, based on the optimal effective time difference selection criteria for different fault areas, steps such as filtering VF wave head paths and simulating virtual fault TWHs yield the optimal time difference matrix for the VFs. The accuracy of the VF location is assessed by calculating the degree of difference between the time difference information of virtual and real faults. An optimization algorithm continuously adjusts the VF point location and TW speed, gradually converging the VF point to the real fault point until they coincide, at which point the VF location corresponds to the real fault location. Verified by the PSCAD simulation platform, the proposed method can achieve precise fault location under various fault types, transition resistances, and noise interferences.

This article presents a novel traveling wave (TW) fault location scheme suitable for multi-terminal transmission line. Firstly, Successive Variational Mode Decomposition (SVMD) and Teager Energy Operator (TEO) are used for feature extraction, and the TW velocity and TW arrival time are obtained. Secondly, the fault section identification method is derived based on the perspective of frequency-dependent TW velocity instead of constant one, and the intrinsic distance matrix (IDM), fault distance matrix (FDM) and fault section identification matrix (FSIM) are defined. Thirdly, the fault section and reference terminal are identified by analyzing the characteristic of elements in FSIM. Finally, the estimated location is determined according to the average value of fault distances calculated on the paths containing the selected reference terminal and real fault point. Extensive simulation studies are conducted in PSCAD/EMTDC, and the test results demonstrated that the proposed method is feasible and reliable under various fault situations.

This paper discusses a device‐level implementation of a travelling wave (TW) protection device (PD) designed for a real low‐voltage DC microgrid. The TWPD fault detection and location algorithm is executed on a commercial digital signal processor (DSP) board, involving signal sampling at 1 MHz via the DSP board's analog‐to‐digital converter (ADC). The analogue input card measures positive pole, negative pole and pole‐to‐pole voltages at the TWPD location. Upon a successful fault detection using a second‐order high‐pass filter, the voltage data is normalised and multi‐resolution analysis (MRA) is performed on a 128‐sample buffer around the TW arrival time. MRA employs the discrete wavelet transform (DWT) to capture high‐frequency voltage patterns, and then the Parseval's energy theorem quantifies these TW characteristics by computing the energy of reconstructed wavelet coefficients. These energy values per decomposed frequency band are the basis for training a random forest classifier that predicts fault location and type. The TWPD is fully implemented and connected to a real DC microgrid in Albuquerque, NM, USA, for validation, and results are shown for field tests verifying the performance under faults.

… This study aims to improve fault location accuracy, by … traveling wave signals and an improved traveling wave fault location … , travelling wave protection, travelling wave fault location and …

Addressing the challenges brought by complex hybrid ring networks in fault section location and accurate location, this article proposes a hybrid transmission network traveling wave (TW) location method based on fault deduction and wave speed optimization. First, this article analyzes the TW propagation process and establishes equivalent models for each stage of the propagation process, deriving initial TW relationships between measurement points. Based on this, the fault deduction algorithm determines the TW propagation paths, enabling network decomposition of complex hybrid ring lines. Combining the concept of virtual fault (VF), a fault section location model is established, and the fault section is determined by maximizing the similarity of VF waveforms. Utilizing the TW arrival time in nonfault areas after network decomposition, a TW speed optimization model is established to obtain the optimal TW speed solution for the region, achieving accurate location. Verified by the PSCAD simulation platform, this method can accurately locate the fault section and determine the optimal TW speed in complex hybrid ring network scenarios, achieving accurate fault location.

Short circuits might occur anywhere in the power grid, so high-speed fault clearing, and fault locating are significant to maintain grid stability, safety, and reliability. Since most faults are transient, the auto-reclose (AR) scheme is widely incorporated to lessen outage duration, avoid extensive outages, and improve grid stability. Meanwhile, a hybrid transmission line (HTL) integrating overhead line (OHL) and undersurface cable is utilised globally in geographically constrained situations. Protecting HTL with the traditional method would ban the use of the AR scheme due to the hardship of ensuring the fault location. Moreover, traveling-wave fault location (TWFL) technology has evolved from grid monitoring into practical use in protective relaying to improve fault locating function in the grid. In the case of the Sumatra- Bangka 150 kV interconnection, the geographical condition led to the use of HTL composed of overhead lines, underground cables, and undersea cables. Therefore, this paper investigates the performance and accuracy of the TWFL to allow the AR scheme on fault along the OHL according to actual fault records. The results show that the accuracy of the TWFL is satisfying, although its performance is affected by the non-homogenous material throughout the HTL.

This paper proposes a novel single-ended traveling wave-based fault location method for unearthed overhead lines with a complex topology. The low fault current magnitudes of a phase-to-ground fault in unearthed sub-transmission networks make ineffective the operation of the impedance-based protection. The proposed method decouples the dependence of the traveling waves propagation modes on the speed propagation of the line, to release the analysis of a multi-branched non-homogeneous line by means of a single measurement terminal. The combination of the results obtained by analyzing different propagation modes allows to determine the fault distance with errors in the order of tens of meters. The proposed method has been applied to a multi-terminal and branched unearthed overhead line simulated in EMTP-RV environment to identify 20 different faults, 10 before the line branch and 10 after it. In the majority of the tested fault cases, the proposed technique allows identifying the fault location with an average error lower than 0.3%. For faults in critical positions, for instance close to the line branch or to the measurement terminal, the average error is lower than 10%.

The detection and selection of fault lines in resonant grounding distribution networks pose challenges due to the lack of sufficient state parameters and data. This paper proposes an approach to overcome these limitations by reconstructing the initial criterion for fault occurrence and fault line selection. Firstly, a combination of 15% of the traditional phase voltage and the sum of the zero-sequence voltage gradient is suggested as the initial criterion for fault occurrence. This improves the speed of the line selection device. Additionally, the transient process of high-resistance grounding in a resonant grounding system is analyzed based on the impedance characteristics of high- and low-frequency lines. The line selection criterion is then established by comparing the current and voltage derivative waveforms on high- and low-frequency lines. To verify the effectiveness of the proposed method, simulations are conducted. The results demonstrate that this method can effectively handle high-resistance grounding faults under complex conditions while meeting the required speed for line selection.

At present, the most common faults in low current ground fault systems are single-phase short-circuit faults. The prolonged operation of the system with faults poses a significant threat to the safety of the power grid. Faced with the complex network structure, diverse operating modes, numerous branch lines, and varied load characteristics in power systems, the use of a single principle for fault line selection often leads to slow line selection, high difficulty, and low accuracy. Therefore, it is of great significance to study a line selection method suitable for low current ground fault systems that can quickly, accurately, and reliably identify the faulted lines. In this paper, based on the electromagnetic induction law and utilizing the mixed injection method of AC and DC signals, the macroscopic fault area is determined, and fault point detection is performed.

This paper presents an extensive review of the most effective and modern monitoring methods for electrical power lines, with particular attention to high-voltage (HV) and medium-voltage (MV) systems. From a general point of view, the main objective of these techniques is to prevent catastrophic failures by detecting the partial damage or deterioration of components and allowing maintenance operations to be organized. In fact, the protection devices commonly used in transmission and distribution networks guarantee the location of faults, such as short-circuits, putting the non-functioning branch of the network out of service. Nowadays, alongside these devices, it is possible to introduce new intelligent algorithms capable of avoiding the total loss of functionality, thus improving the reliability of the entire network. This is one of the main challenges in modern smart grids, which are characterized by the massive integration of renewable energy sources and a high level of complexity. Therefore, in the first part of this paper, a general overview of the most common protection devices is proposed, followed by an analysis of the most modern prevention algorithms. In the first case, the coordination of the relays plays a fundamental role in obtaining the fault location with a high level of selectivity, while in the field of preventive analysis, it is necessary to address the implementation of artificial intelligence methods. The techniques presented in this paper provide a comprehensive description of the different monitoring approaches currently used in distribution and transmission lines, highlighting the coordination of protection relays, the computational algorithms capable of preventing failures, and the influence of the distributed generation in their management. Therefore, this paper offers an overview of the main diagnostic techniques and protection devices, highlights the critical issues that can be overcome through the introduction of artificial intelligence, and describes the main prognostic methods, focusing on their invasive level and the possibility of operating directly online. This work also highlights the main guidelines for the classification and choice between the different approaches.

… critical challenge is low current contribution to the fault, which … Issues in grounding and fault current amplitude reduction … the possibility of fault occurrence, and a proper selection of inter…

This study presents a novel approach that employs a mixture of the tunable-Q wavelet transform (TQWT) and enhanced AdaBoost to address the issue of high impedance fault (HIF) recognition in power distribution networks. Traditional overcurrent protection relays frequently have lower fault current levels than normal current, making it exceedingly difficult to detect this HIF problem with the necessity to use a quick and effective approach to find HIF problems. Since the TQWT performs better with signals that exhibit oscillatory behavior, it has been utilized to extract special features for the training of the improved AdaBoost model. The procedure is accelerated by calculating the Kourtosis (K) value for each level and selecting the ideal level of decomposition to minimize computing work. Faulted zones are categorized using an enhanced AdaBoost approach. Under normal, noisy, and unbalanced conditions, the recommended approach is applied to an imbalanced 123-bus test system and an IEEE 33-bus test system. The efficiency of the recommended method is also being assessed for imbalanced distribution networks incorporating dispersed generation into real-time platforms. This procedure is quick compared to previous methods since it uses an upgraded AdaBoost classifier and optimal decomposition level.

The diagnosis of high-impedance fault (HIF) is a critical challenge due to the presence of faint signals that exhibit distortion and randomness. In this study, we propose a novel …

Introduction: With the development of artificial intelligence technology, more and more fields are applying deep learning and reinforcement learning techniques to solve practical problems. In the power system, both the direct current (DC) power system and the power grid substation are important components, and their reliability and stability are crucial for production efficiency and safety. The power grid substation is used to convert power from high-voltage transmission lines to low-voltage transmission lines, or from alternating current to direct current (or vice versa), in order to efficiently transmit and distribute power in the power system. However, diagnosing faults and designing cascaded protection strategies has always been a challenge due to the complexityand uncertainty of the DC power system.Methods: To improve the reliability and stability of the DC power system and power grid substation, this paper aims to develop an intelligent fault diagnosis system and cascaded protection strategy to reduce faults and downtime, lower maintenance costs, and increase production efficiency. We propose a method based on reinforcement learning and a convolutional neural network-long short-term memory (CNN-LSTM) model for fault diagnosis and cascaded protection strategy design in the DC power system. CNN is used to extract features from raw data, while LSTM models time-series data. In addition, we use reinforcement learning to design cascaded protection strategies to protect the power system from the impact of faults.Results: We tested our method using real 220V DC power system data in experiments. The results show that our method can effectively diagnose faults in the DC power system and formulate effective cascaded protection strategies.Discussion: Compared with traditional methods, this intelligent method can diagnose faults faster and more accurately, and formulate better cascaded protection strategies. This method helps reduce maintenance costs, increase production efficiency, and can be applied to other fields.

Fault detection, classification, and precise location identification in power transmission lines are critical issues for energy transmission and power systems. Accurate fault diagnosis is essential for system stability and safety as it enables rapid problem resolution and minimizes interruptions in electrical energy supply. The characteristic parameters of mixed-conductor power transmission lines connected to the grid were calculated using the relevant line data. Based on these parameters, a dataset was created with computer-derived values. This dataset included variations in arc resistance and the short circuit power of the corresponding bus, facilitating the performance testing of various machine learning algorithms. It was observed that the correct determination of the faulty phase was of high importance in the correct determination of the fault position. For this reason, a gradual structure was preferred. It was achieved with a 100 percent success rate in fault detection with the ensemble bagged algorithm. It was obtained with the neural network algorithm with a 99.97 percent success rate in faulty phase detection. The most successful location results were obtained with the interaction linear algorithm with 0.0066 MAE for phase-to-phase faults and the stepwise linear algorithm with 0.0308 MAE for phase ground faults. Using the proposed algorithm, fault locations were identified with a maximum error of 26 m for phase-to-ground faults and 110 m for phase-to-phase faults on a transmission line with a mixed conductor of approximately 178 km. Additionally, we compared the training and testing results of several machine learning algorithms metrics including the accuracy, total error, mean absolute error, root mean square, and root mean square error to provide informed recommendations based on their performance. The findings aim to guide users in selecting the most effective machine learning models for predicting failures in transmission lines.

… fault location in power networks that incorporates the presence of D-STATCOM and considers the effect of line … fault voltage magnitude at the substation and comparing it with simulated …

… location methods. This paper presents a novel single-ended time … fault location method for single-phase-to-ground faults, one which fully considers the distributed parameters of the line …

… fault location approach for overhead transmission line is widely applied in actual power grid, which has better fault location … However, travelling wave fault location approach is highly …

Aiming at the difficulty of fault location of multi-source transmission lines, this paper proposes a fault location method for multi-terminal transmission lines based on a fault branch judgment matrix. The fault traveling wave signal is decomposed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the IMFs sensitive components that can characterize the fault characteristics of the target signals are selected by constructing a correlation-rearrangement entropy function. The arrival time of fault signals at the endpoint has been accurately calibrated by combining them with the Teager Energy Operator (TEO). To eliminate the influence of wave velocity and fault time on the location results, this paper proposes a two-terminal location method based on the line mode component to improve the location accuracy. On this basis, combined with the fault branch judgment matrix, the accurate location of multi-terminal transmission line faults is realized. This method has been shown to have high accuracy in detecting traveling wave heads, accurately judging fault branches, and producing a small error in fault location results. Compared with the existing multi-terminal transmission line fault location algorithm, it has obvious advantages and meets the needs of actual working conditions.

… This paper aims to reveal conventional impedance-based fault location methods’ … specify the fault location in the lines emanating from the IBRs. The proposed fault location formulation …

The precise identification of fault locations in multiterminal dc (MTDC) transmission systems using traveling waves (TWs) is imperative for ensuring the secure and stable operation of power systems. The presence of wave impedance discontinuity in overhead-cable hybrid transmission lines (OCHTLs) causes significant changes in TW velocity. Traditional TW-based fault location methods struggle to adapt to these changes. This article introduces an innovative fault location approach tailored for OCHTL, with the primary aim of enhancing fault-ranging accuracy. Initially, a typical OCHTL equivalent model is established, and the propagation characteristics of the OCHTLs’ fault voltage TW (VTW) are analyzed. The variational mode decomposition-Teager energy operator (VMD-TEO) algorithm is then employed to determine the arrival time of the VTW wavefront. To optimize VMD parameters, the Kullback–Leibler (K–L) divergence method is applied, addressing modal aliasing. Additionally, we explore TW velocity attenuation characteristics across segments and introduce a frequency-modified algorithm for faulty segment identification. A high-precision fault location algorithm is then proposed based on two-terminal information, and its robustness and accuracy are validated through simulations using a PSCAD-built MTDC system with OCHTLs.

… to impedance-based fault location in lines interconnecting IBRs to the grid… fault location methods is evaluated, and a multi-method methodology that minimizes the obtained fault location …

Providing continuous electric power supply to consumers is difficult for power system engineers due to various faults in transmission and distribution systems. Precise fault location (FL) in transmission lines speeds up the repair and restoration process. This paper proposes a wide neural network (WNN)Wide neural network approach for FL identification in power transmission networks. The proposed WNN uses the voltage, current magnitude, and phase angles measured by the phasor measurement unit (PMU)Phasor measurement unit. This proposed work considers the Western System Coordinating Council (WSCC)Western system coordinating council 9-bus test system with optimal PMU placement for fault analysis. The several types of faults created on different line sections of the test system are simulated using MATLAB/Simulink environment considering various fault parameters such as fault resistance, fault inception angle, and fault distance. The performance of the proposed scheme is measured by finding the absolute prediction error between actual and predicted FL. The results show that the average prediction errors for L-G, LL, LL-G, and LLL faults are 0.0121, 0.0209, 0.0139, and 0.0124, respectively. The proposed method outperforms the related machine learning-based FL estimation schemes for all the test cases considered at different fault locations. In addition, considering phase angle measurement improves the accuracy of finding the fault location compared to the voltage magnitude and current magnitude feature set.

The tower structure and geometrical arrangement of transmission line conductors in the power system depend on various technical, economic, and geographical factors and the age of construction of the lines. Thus, the arrangement of lines may not be transposed or follow the standards of the electricity industry. The untransposed conductors cause asymmetric couplings in the transmission lines, directly leading to destructive effect on the function of distance protection and accurate determination of the fault location. Fault location in untransposed transmission lines is presented as a novel research piece by investigating the circuit equations of positive-negative-zero sequences and using synchronous voltage measurements taken from the near and far terminals of the transmission line together with measuring the current of one terminal. In this algorithm, the effect of mutual interphase impedance and admittance due to the untransposed structure of the line is fully taken into account. Fault location equations are designed based on the complete equivalent circuit of the untransposed transmission line using the modeling of all effective parameters. As the presented design adopts the current measurements of one terminal, the destructive effect of the current measurement systems is dropped. The Simulink model of the suggested design has been implemented in Digsilent Power Factory software and the algorithm has been programmed in MATLAB software. The performance of the proposed algorithm has been tested and evaluated for a two-terminal network in normal and critical fault conditions, as well as for a 39-bus untransposed New England network. According to the obtained results, the average estimation error of all scenarios is approximately equal to 0.07%. The results presented in the simulation and sensitivity analysis section confirm the accurate and correct performance of the algorithm.

… This paper proposes an Extreme Learning Machine-based fault location method to evaluate the DC transmission line faults of Terminal-hybrid LCC-VSC-HVDC systems. For this …

… fault location observability rules associated with some important impedance-based fault location … that indicates the number of transmission lines that fault location on them is not possible…

… of a multi-task training approach for locating faults and their type. With the use of two IEEE … , the proposed fault diagnosis model is tested. Examining various setups for fault analysis …

The accurate diagnosis of transmission line fault types is a prerequisite for quickly removing faults and restoring power supply, as well as the key to effectively reducing user economic losses, ensuring stable operation of the power system. The rapid development of artificial intelligence technology has been a promising way for fault diagnosis. However, the existing methods are still limited by small simples and accuracy of generalization. To overcome these problems, a transmission line fault diagnosis method based on an improved multiple SVM (MSVM) model is proposed in this paper. Firstly, the transmission line was selected as the research object, and its fault types and causes were analyzed in detail. Then, typical fault information are selected and corresponding MSVM model is established. Meanwhile, genetic algorithm (GA) is adopted to optimize model parameters to improve the accuracy of generalization. Finally, an improved IEEE-30 node test system and a real-world testing data are adopted to verify the accuracy and feasibility of the proposed method. Through analysis, fault diagnosis accuracy of the proposed method can be improved by up to 11% with better fitness value.

… learning-based fault location method for VSC-HVDC transmission lines and discussed its … fault locations, and fine-tunes the model with small data sets from the target transmission line …

This paper proposes graph analysis methods to fully automate the fault location identification task in power distribution systems. The proposed methods take basic unordered data from power distribution systems as input, including branch parameters, load values, and the location of measuring devices. The proposed data preparation and analysis methods automatically identify the system's topology and extract essential information, such as faulted paths, structures, loading of laterals and sublaterals, and estimate the fault location accordingly. The proposed graph analysis methods do not require complex node and branch numbering processes or renumbering following changes in the system topology. The proposed methods eliminate the need for human intervention at any step of the fault location identification process. They are scalable and applicable to systems of any size. The performance of the proposed algorithm is demonstrated using the IEEE 34-bus distribution test system.

This paper proposes a time-domain fault location identification method for mixed overhead-underground power distribution systems that can handle challenging fault scenarios such as sub-cycle faults, arcing faults and evolving faults. The proposed method is formulated based on differential equations of the system and accounts for the peculiarities of power distribution systems with distributed generations. It considers the presence of loads, multi-phase laterals and sub-laterals, heterogenous overhead and underground lines, and infeeds and remote-end fault current contributions of distributed generations. It utilizes data collected by limited number of measuring devices installed in modern power distribution systems to systematically eliminate possible multiple fault location estimations to provide a single correct estimation of the actual location of the fault. The performance of the proposed method is demonstrated using IEEE 34-node test system.

The growing penetration of renewable and distributed generation is transforming power systems and challenging conventional protection schemes that rely on fixed settings and local measurements. Machine learning (ML) offers a data-driven alternative for centralized fault classification (FC) and fault localization (FL), enabling faster and more adaptive decision-making. However, practical deployment critically depends on robustness. Protection algorithms must remain reliable even when confronted with missing, noisy, or degraded sensor data. This work introduces a unified framework for systematically evaluating the robustness of ML models in power system protection. High-fidelity EMT simulations are used to model realistic degradation scenarios, including sensor outages, reduced sampling rates, and transient communication losses. The framework provides a consistent methodology for benchmarking models, quantifying the impact of limited observability, and identifying critical measurement channels required for resilient operation. Results show that FC remains highly stable under most degradation types but drops by about 13% under single-phase loss, while FL is more sensitive overall, with voltage loss increasing localization error by over 150%. These findings offer actionable guidance for robustness-aware design of future ML-assisted protection systems.

Accurate fault detection and localization in electrical distribution systems is crucial, especially with the increasing integration of distributed energy resources (DERs), which inject greater variability and complexity into grid operations. In this study, FaultXformer is proposed, a Transformer encoder-based architecture developed for automatic fault analysis using real-time current data obtained from phasor measurement unit (PMU). The approach utilizes time-series current data to initially extract rich temporal information in stage 1, which is crucial for identifying the fault type and precisely determining its location across multiple nodes. In Stage 2, these extracted features are processed to differentiate among distinct fault types and identify the respective fault location within the distribution system. Thus, this dual-stage transformer encoder pipeline enables high-fidelity representation learning, considerably boosting the performance of the work. The model was validated on a dataset generated from the IEEE 13-node test feeder, simulated with 20 separate fault locations and several DER integration scenarios, utilizing current measurements from four strategically located PMUs. To demonstrate robust performance evaluation, stratified 10-fold cross-validation is performed. FaultXformer achieved average accuracies of 98.76% in fault type classification and 98.92% in fault location identification across cross-validation, consistently surpassing conventional deep learning baselines convolutional neural network (CNN), recurrent neural network (RNN). long short-term memory (LSTM) by 1.70%, 34.95%, and 2.04% in classification accuracy and by 10.82%, 40.89%, and 6.27% in location accuracy, respectively. These results demonstrate the efficacy of the proposed model with significant DER penetration.

In this paper, we present a data-driven Forward Selection with Neighborhood Refinement (FSNR) algorithm to determine the number and placement of Phasor Measurement Units (PMUs) for maximizing deep-learning-based fault diagnosis performance. Candidate PMU locations are ranked via a cross-validated Support Vector Machine (SVM) classifier, and each selection is refined through local neighborhood exploration to produce a near-optimal sensor set. The resulting PMU subset is then supplied to a 1D Convolutional Neural Network (CNN) for faulted-line localization and fault-type classification from time-series measurements. Evaluation on modified IEEE 34- and IEEE 123-bus systems demonstrates that the proposed FSNR-SVM method identifies a minimal PMU configuration that achieves the best overall CNN performance, attaining over 96 percent accuracy in fault location and over 99 percent accuracy in fault-type classification on the IEEE 34 system, and approximately 94 percent accuracy in fault location and around 99.8 percent accuracy in fault-type classification on the IEEE 123 system.

This paper presents fault detection and classification using Wavelet and ANN based methods in a DFIG-based series compensated system. The state-of-the art methods include Wavelet transform, Fourier transform, and Wavelet-neuro fuzzy methods-based system for fault detection and classification. However, the accuracy of these state-of-the-art methods diminishes during variable conditions such as changes in wind speed, high impedance faults, and the changes in the series compensation level. Specifically, in Wavelet transform based methods, the threshold values need to be adapted based on the variable field conditions. To solve this problem, this paper has proposed a Wavelet-ANN based fault detection method where Wavelet is used as an identifier and ANN is used as a classifier for detecting various fault cases. This methodology is also effective under SSR condition. The proposed methodology is evaluated on various fault and non-fault cases generated on an IEEE first benchmark model under varying compensation levels from 20% to 55%, impedance faults, and wind velocity from 6m/sec to 10m/sec using MATLAB/Simulink, OPALRT(OP4510) manufactured real-time digital simulator environment, Arduino board I/O ports communicating with external PC in which ANN model dumped, using Arduino support package of MATLAB. The preliminary results are compared with the state-of-the-art fault detection method, where the proposed method shows robust performance under varying field conditions.

Diagnosis in PV systems aims to detect, locate and identify faults. Diagnosing these faults is vital to guarantee energy production and extend the useful life of PV power plants. In the literature, multiple machine learning approaches have been proposed for this purpose. However, few of these works have paid special attention to the detection of fine faults and the specialized process of extraction and selection of features for their classification. A fine fault is one whose characteristic signature is difficult to distinguish to that of a healthy panel. As a contribution to the detection of fine faults (especially of the snail trail type), this article proposes an innovative approach based on the Random Forest (RF) algorithm. This approach uses a complex feature extraction and selection method that improves the computational time of fault classification while maintaining high accuracy.

Accurate fault location is essential for operational reliability and fast restoration in wind farm collector networks. However, the growing integration of inverter-based resources changes the current and voltage behavior during faults, challenging the effectiveness of traditional phasor-based diagnostic methods. In this context, the present paper introduces an advanced machine-learning solution that enhances a deterministic fault distance estimator by incorporating a correction model driven by a Gated Residual Network, specifically designed to minimize residual fault location errors. Through comprehensive feature engineering and selection processes, an improved predictor was developed and trained on a diverse set of fault scenarios simulated in a PSCAD-based real-world wind farm model, including variations in fault type, resistance, location, inception angle, and generation penetration. Hyperparameter optimization was performed using the Optuna framework, and the robustness of the method was statistically validated. Results show a significant improvement in accuracy, with a 76% overall decrease in fault location error compared to state-of-the-art approaches. The proposed method demonstrates strong scalability and adaptability to topological and operational changes. This approach advances the deployment of data-driven fault location frameworks for modern power systems.

The review begins with an exploration of acceptable cable types guided by local standards. It then investigates typical cable faults, including insulation degradation, conductor faults, and ground faults, providing insights into their characteristics, causes, and detection methods. Furthermore, the manuscript surveys the latest publications and standards on DSP techniques in fault location spanning various algorithms used. This review provides a comprehensive understanding of low and medium-voltage cables, fault types, and DSP techniques. The findings contribute to improved fault diagnosis and localization methods, facilitating more accurate and efficient cable fault management strategies

Electrical fault classification is vital for ensuring the reliability and safety of power systems. Accurate and efficient fault classification methods are essential for timely and effective maintenance. In this paper, we propose a novel approach for effective fault classification through Grassmann manifolds, which is a non-Euclidean space that captures the intrinsic structure of high-dimensional data and offers a robust framework for feature extraction. We use simulated data for electrical distribution systems with various types of electrical faults. The proposed method involves transforming the measurement fault data into Grassmann manifold space using techniques from differential geometry. This transformation aids in uncovering the underlying fault patterns and reducing the computational complexity of subsequent classification steps. To achieve fault classification, we employ a machine learning technique optimized for the Grassmann manifold. The support vector machine classifier is adapted to operate within the Grassmann manifold space, enabling effective discrimination between different fault classes. The results illustrate the efficacy of the proposed Grassmann manifold-based approach for electrical fault classification which showcases its ability to accurately differentiate between various fault types.

In this document, the supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurement chain modeling will be studied, where the measurement error sources of each component in the SCADA and PMU measurement chains and the reasons leading to measurement errors exhibiting non-zero-mean, non-Gaussian, and time-varying statistical characteristic are summarized and analyzed. This document provides a few equations, figures, and discussions about the details of the SCADA and PMU measurement error chain modeling, which are intended to facilitate the understanding of how the measurement errors are designed for each component in the SCADA and PMU measurement chains. The measurement chain models described here are also used for synthesizing measurement errors with realistic characteristics in simulation cases to test the developed algorithms or methodologies.

This work investigates the reduction of phasor measurement unit (PMU) data through low-rank matrix approximations. To reconstruct a PMU data matrix from fewer measurements, we propose the framework of interpolatory matrix decompositions (IDs). In contrast to methods relying on principal component analysis or singular value decomposition, IDs recover the complete data matrix using only a few of its rows (PMU datastreams) and/or a few of its columns (snapshots in time). This compression enables the real-time monitoring of power transmission systems using a limited number of measurements, thereby minimizing communication bandwidth. The ID perspective gives a rigorous error bound on the quality of the data compression. We propose selecting rows and columns used in an ID via the discrete empirical interpolation method (DEIM), a greedy algorithm that aims to control the error bound. This bound leads to a computable estimate for the reconstruction error during online operations. A violation of this estimate suggests a change in the system's operating conditions, and thus serves as a tool for fault detection. Numerical tests using synthetic PMU data illustrate DEIM's excellent performance for data compression, and validate the proposed DEIM-based fault-detection method.

The recent increase in renewable energy penetration at the distribution level introduces a multi-directional power flow that outdated traditional fault location techniques. To this extent, the development of new methods is needed to ensure fast and accurate fault localization and, hence, strengthen power system reliability. This paper proposes a data-driven ground fault location method for the power distribution system. An 11-bus 20 kV power system is modeled in Matlab/Simulink to simulate ground faults. The faults are generated at different locations and under various system operational states. Time-domain faulted three-phase voltages at the system substation are then analyzed with discrete wavelet transform. Statistical quantities of the processed data are eventually used to train an Artificial Neural Network (ANN) to find a mapping between computed voltage features and faults. Specifically, three ANNs allow the prediction of faulted phase, faulted branch, and fault distance from the system substation separately. According to the results, the method shows good potential, with a total relative error of 0,4% for fault distance prediction. The method is applied to datasets with unknown system states to test robustness.

The increasing integration of Inverter-Based Resources (IBRs) is reshaping fault current characteristics, presenting significant challenges to traditional protection and fault location methods. This paper addresses a key limitation in fault location within wind farm collector networks, i.e., one-terminal phasor-based methods become inaccurate when IBRs are electrically located downstream from the fault. In such cases, the voltage drop caused by IBR fault current injections is not captured by the Intelligent Electronic Device, resulting in a systematic overestimation of fault distance. To mitigate this issue, a general compensation framework was proposed by augmenting classical loop formulations with a distance-dependent voltage correction term. The methodology was derived analytically using a sequence-domain representation and generalized to multiple fault types through a unified notation. It maintains the simplicity and interpretability of conventional approaches and can be implemented using only local measurements. The method was evaluated through EMT simulations in PSCAD using a realistic wind farm model. Results show significant improvements in location accuracy, with average and maximum errors notably reduced, especially for ground-involved faults where reductions exceed 90\%. Furthermore, the compensation eliminates sensitivity to wind penetration levels and ensures uniform performance across feeders, positioning the method as a practical solution for modern renewable-dominated grids.

Identifying faulty lines and their accurate location is key for rapidly restoring distribution systems. This will become a greater challenge as the penetration of power electronics increases, and contingencies are seen across larger areas. This paper proposes a single terminal methodology (i.e., no communication involved) that is robust to variations of key parameters (e.g., sampling frequency, system parameters, etc.) and performs particularly well for low resistance faults that constitute the majority of faults in low voltage DC systems. The proposed method uses local measurements to estimate the current caused by the other terminals affected by the contingency. This mimics the strategy followed by double terminal methods that require communications and decouples the accuracy of the methodology from the fault resistance. The algorithm takes consecutive voltage and current samples, including the estimated current of the other terminal, into the analysis. This mathematical methodology results in a better accuracy than other single-terminal approaches found in the literature. The robustness of the proposed strategy against different fault resistances and locations is demonstrated using MATLAB simulations.

Data driven transmission line fault location methods have the potential to more accurately locate faults by extracting fault information from available data. However, most of the data driven fault location methods in the literature are not validated by field data for the following reasons. On one hand, the available field data during faults are very limited for one specific transmission line, and using field data for training is close to impossible. On the other hand, if simulation data are utilized for training, the mismatch between the simulation system and the practical system will cause fault location errors. To this end, this paper proposes a physics-informed data-driven fault location method. The data from a practical fault event are first analyzed to extract the ranges of system and fault parameters such as equivalent source impedances, loading conditions, fault inception angles (FIA) and fault resistances. Afterwards, the simulation system is constructed with the ranges of parameters, to generate data for training. This procedure merges the gap between simulation and practical power systems, and at the same time considers the uncertainty of system and fault parameters in practice. The proposed data-driven method does not require system parameters, only requires instantaneous voltage and current measurements at the local terminal, with a low sampling rate of several kHz and a short fault time window of half a cycle before and after the fault occurs. Numerical experiments and field data experiments clearly validate the advantages of the proposed method over existing data driven methods.

This study aims to create a MATLAB simulation model to examine three-phase symmetrical and unsymmetrical faults that frequently occur in long transmission line systems. The types of faults considered include single line to ground fault (L-G), double line to ground fault (L-L-G), triple line to ground fault (L-L-L-G), and line to line fault (L-L). The analysis of these faults and their impact on simulation outputs such as voltage and current are investigated. The research utilizes MATLAB software to simulate the transmission line model. The simulation model provides a valuable tool for studying the behaviour of different fault types and understanding their effects on the electrical system. The results obtained from the simulation experiments can aid in developing effective fault detection and protection strategies for transmission lines.

Arcing faults in low voltage (LV) distribution systems associated with arc-flash risk and potentially significant equipment damage are notoriously difficult to detect under some conditions. Especially so when attempting to detect using sensing at the line, high voltage side of a substation transformer. This paper presents an analytics-based physics-aware approach to detect high-impedance, low-current arcing faults from the primary side of the substation transformer at current thresholds, below normal operating events, along with transformer inrush currents. The proposed methodology leverages the Hankel Alternative View Of Koopman Operator approach to differentiate arcing faults from standard operations, while the Series2Graph method is employed to identify the time of fault occurrence and duration. Unlike prior studies that detect such faults at the device or secondary transformer side, this work demonstrates successful fault detection at the primary side of the distribution substation transformer for faults occurring on the secondary side. The approach addresses the practical challenges of differentiating primary side expected and acceptable transients from similar magnitude LV arcing fault currents that may occur on the secondary side. The results demonstrate the efficacy of the proposed method in accurately identifying fault occurrence and duration, minimizing the risk of false positives during similar characteristic events, thus improving the reliability and operational efficiency of power distribution systems. This approach can benefit both traditional and smart power grids that employ similar transformer configurations.

This article proposes a source-independent method for detecting faults along Transmission Lines (TL) to reduce the protection issues arising from Inverter-Based Resources (IBRs). In the proposed method, high-frequency waves are sent from either end of a TL, and the amplitudes of the receiving waves at the other end are measured. Faults change the characteristics of TLs. Therefore, the amplitudes of the receiving waves differ when a fault occurs. Closed-form formulations are developed for describing the receiving waves before and during the faults. These formulations indicate that at least one of the receiving waves is reduced after fault inception. Therefore, faults can be detected by identifying a decrease in one of the receiving waves. To evaluate the performance of the proposed method, EMTP-RV is utilized for performing simulations. Additionally, laboratory experiments are conducted for further evaluation of the proposed method. The simulation and experimental results demonstrate that the proposed method is able to detect faults along TLs regardless of the sources supplying the grid.

This paper proposes a source-independent method for the detection and classification of faults along Transmission Lines (TLs). It aims to reduce the protection issues arising from Inverter-Based Resources (IBRs). Inspired by Power Line Communication (PLC), the proposed method utilizes high-frequency carrier waves which are sent from either side of a TL over each phase. As faults disrupt the propagation of carriers, the receiving carrier waves before and during faults exhibit differences. Based on this principle, the proposed method continuously compares the receiving carrier waves with a short history of them to detect and classify faults. The performance of the proposed method was evaluated using EMTP-RV and MATLAB, and compared to traditional phasor-based distance relays. The simulation results confirm the capability of the proposed method in detection and classification of different faults regardless of power sources types.

Motivated by the need to localise faults along electrical power lines, this paper adopts a frequency-domain approach to parameter estimation for an infinite-dimensional linear dynamical system with one spatial variable. Since the time of the fault is unknown, and voltages and currents are measured at only one end of the line, distance information must be extracted from the post-fault transients. To properly account for high-frequency transient behaviour, the line dynamics is modelled directly by the Telegrapher's equation, rather than the more commonly used lumped-parameter approximations. First, the governing equations are non-dimensionalised to avoid ill-conditioning. A closed-form expression for the transfer function is then derived. Finally, nonlinear least-squares optimisation is employed to search for the fault location. Requirements on fault bandwidth, sensor bandwidth and simulation time-step are also presented. The result is a novel end-to-end algorithm for data generation and fault localisation, the effectiveness of which is demonstrated via simulation.

This letter proposes an alternative underdetermined framework for fault location that utilizes current measurements along with the branch-bus matrix, providing another option besides the traditional voltage-based methods. To enhance fault location accuracy in the presence of multiple outliers, the robust YALL1 algorithm is used to resist outlier interference and accurately recover the sparse vector, thereby pinpointing the fault precisely. The results on the IEEE 39-bus test system demonstrate the effectiveness and robustness of the proposed method.