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During the sintering process in iron production, wheel swing is a sign of sintering machine trolley axle faults, which may lead to the wheel falling off and affect the production operation of the sintering machine system in serious cases. To solve this problem, this paper proposes a fault detection and localization method based on the You Only Look Once version 9 (YOLOv9) object detection algorithm and frame difference method for detecting sintering machine trolley wheel swing. The wheel images transmitted from the camera were sent to a trolley wheel and side panel number detection model that was trained on YOLOv9 for recognition. The wheel recognition boxes of the previous and subsequent frames were fused into the wheel region of interest. In the wheel region of interest, the difference operation was carried out. The result of the difference operation was compared with the preset threshold to determine whether the trolley wheel swings. When a wheel swing fault occurs, the image of the side plate at the time of the fault is collected, and the number on the side plate is identified so as to accurately locate the faulty trolley and to assist the field personnel in troubleshooting the fault. The experimental results show that this method can detect wheel swing faults in the industrial field, and the detection accuracy of wheel swing faults was 93.33%. The trolley side plate numbers' average precision was 99.2% in fault localization. Utilizing the aforementioned method to construct a system for detecting wheel swing can provide technical support for fault detection of the trolley axle on the sintering machine. [ABSTRACT FROM AUTHOR]

In the practical production environment, the complexity and variability of hydroelectric units often result in a need for more fault data, leading to inadequate accuracy in fault identification for data-driven intelligent diagnostic models. To address this issue, this paper introduces a novel fault diagnosis method tailored for unbalanced small-sample states in hydroelectric units based on the Wasserstein generative adversarial network (W-GAN). Firstly, the fast Fourier transform is used to convert the signal from the time domain to the frequency domain to obtain the spectral data, and the W-GAN is trained to generate false spectral data with the same probability distribution as the real fault data, which are combined with the actual data and inputted into the 1D-CNN for feature extraction and fault diagnosis. In order to assess the effectiveness of the proposed model, a case study was conducted using actual data from a domestic hydropower plant, and the experimental results show that the sample features can be effectively enriched via data enhancement performed on small-sample data to improve the accuracy of fault diagnosis, which verifies the effectiveness of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

Objective. The processing and analysis of medical rehabilitation information data in tertiary hospitals is a hot research topic. Combining medical data analysis with machine learning algorithms to improve data mining efficiency is a problem that needs to be solved at present. This paper proposes an autonomous perception model of sports medicine rehabilitation equipment based on a deep learning algorithm for sports medical rehabilitation data. Methods. This paper cites a deep learning multi-dimensional perception model for medical rehabilitation equipment autonomous perception. The model utilizes the automatic overhaul of medical rehabilitation equipment based on deep belief networks. This paper extracts features through a multi-layer neural network and obtains fault location results of medical rehabilitation equipment through softmax. Results. In similarity prediction, the accuracy rate of the first three kinds of feedback containing the target answer is 77%. The accuracy rate of the target answers included in the top five kinds of feedback was 92%. Conclusion. In this study, it is feasible to apply deep learning to the quality control information system of sports rehabilitation medical equipment. This improves the management efficiency of medical rehabilitation equipment to a certain extent. [ABSTRACT FROM AUTHOR]

To address challenges in locating high-impedance grounding faults (HIGFs) and isolating fault areas in resonant grounding systems, this paper proposes a novel fault identification method based on coordinating a Peterson coil and a resistance grounding system. This method ensures power supply reliability by extinguishing the fault arc during transient faults with the Peterson coil. When a fault is determined to be permanent, the neutral point switches to a resistance grounding mode, ensuring regular distribution of zero-sequence currents in the network, thereby addressing the challenges of HIGF localization and fault area isolation. Fault calibration and nature determination rely on recognizing neutral point displacement voltage waveforms and dynamic characteristics, eliminating interference from asymmetric phase voltage variations. Fault area identification involves assessing the polarity of zero-sequence current waveforms attenuation during grounding mode switching, preventing misjudgments in grounding protection due to random initial fault angles and Peterson coil compensation states. Field experiments validate the feasibility of this fault location method and its control strategy. [ABSTRACT FROM AUTHOR]

As the terminal of the power system, the distribution network is the main area where failures occur. In addition, with the integration of distributed generation, the traditional distribution network becomes more complex, rendering the conventional fault location algorithms based on a single power supply obsolete. Therefore, it is necessary to seek a new algorithm to locate the fault of the distributed power distribution network. In existing fault localization algorithms for distribution networks, since there are only two states of line faults, which can usually be represented by 0 and 1, most algorithms use discrete algorithms with this characteristic for iterative optimization. Therefore, this paper combines the advantages of the particle swarm algorithm and genetic algorithm and uses continuous real numbers for iteration to construct a successive particle swarm genetic algorithm (SPSO-GA) different from previous algorithms. The accuracy, speed, and fault tolerance of SPSO-GA, discrete particle swarm Genetic algorithm, and artificial fish swarm algorithm are compared in an IEEE33-node distribution network with the distributed power supply. The simulation results show that the SPSO-GA algorithm has high optimization accuracy and stability for single, double, or triple faults. Furthermore, SPSO-GA has a rapid convergence velocity, requires fewer particles, and can locate the fault segment accurately for the distribution network containing distorted information. [ABSTRACT FROM AUTHOR]

With the progressive development of new energy technologies, high-power lithium batteries have been widely used in ship power systems due to their high-power density and low environmental pollution, and they have gradually become one of their main propulsion energy sources. However, the large-scale deployment of lithium batteries has also brought a series of safety problems to ship operations, especially the battery internal short circuit (ISC). Battery ISC faults are very hidden and unpredictable at the initial stage and often fail to be detected in time, ultimately leading to overheating, fire or even an explosion of the ship's power system. Based on this, this paper proposes a fast and accurate method for early-stage ISC fault location and detection of lithium batteries. Initially, voltage variations across the lithium battery packs are quantified using curvilinear Manhattan distances to pinpoint faulty battery units. Subsequently, the localized characteristics of voltage variance among adjacent batteries are leveraged to detect an early-stage ISC fault. Simulation results indicate that the proposed method can quickly and accurately locate the position of 5 Ω, 10 Ω and 15 Ω ISC faulty batteries within the battery pack, as well as detect the abnormal batteries in a timely manner with considerable sensitivity and reliability. [ABSTRACT FROM AUTHOR]

In recent years, deep learning‐based fault diagnosis technology for high‐voltage circuit breakers (HVCB) has advanced significantly, but the working environment of HVCBs is complex, resulting in unsatisfactory fault diagnosis results of HVCBs in noisy environment and existing deep learning methods are difficult to solve this problem. This paper proposes a multi‐channel convolutional neural network combines dense residual structure and attention mechanism to achieve high‐precision and high‐robust diagnosis of HVCBs in noisy backgrounds. A dense residual network is introduced into the convolutional neural network to prevent feature loss during network propagation to preserve the difference information between the network layers as much as possible, Simultaneously, a channel attention mechanism is introduced to adaptively adjust the weights of different convolution channels. The model can extract multi‐scale features from the original signal and fully exploit the intrinsic relationship between the vibration signal and the HVCB's operating state. The experimental results show that the diagnostic method can still meet the requirements of fault diagnosis in the presence of noise, with an average diagnostic accuracy rate of 85.92% when the signal‐to‐noise ratio is −4. The model outperforms the traditional single‐channel model in terms of diagnostic accuracy and stability. [ABSTRACT FROM AUTHOR]

To ensure the safe operation of the power system, it is necessary to monitor and manage all kinds of power facilities. Real-time power monitoring can play a role in the early warning of geographical disasters. At the transmission end, the state detection and fault location of transmission lines are related to the normal operation of the power system. The precise time synchronization of the power system can be guaranteed through the satellite positioning time service, so that the time of various power operation actions is consistent. The accuracy can reach several microseconds, which provides a basis for the analysis of power grid operation. In this paper, by installing BeiDou functional modules in power equipment and developing the BeiDou operation platform for the distribution network, the precise management of power timing and positioning is realized by docking with the geographic information systems platform of the power grid. The three-dimensional sphere error probability (SEP) algorithm is selected to evaluate the positioning error data. Based on the three traditional SEP calculation methods, the maximum likelihood estimation algorithm is optimized and improved, which can achieve nanosecond synchronization accuracy. It can also be applied to other precision of synchronization scenarios of the power system to meet the development needs of the future clock system of the power system. Through the simulation of experimental data and comparison with other algorithms, the accuracy is verified and the reliable estimation of parameters is realized. Considering the information loss caused by dimension reduction evaluation, the simulation analysis in this paper is compared with other methods, and the accuracy is significantly improved. The power system positioning and timing module developed in this paper can not only be used in the power system but also has a certain reference and application value for other devices in the same industry that need timing and positioning services. [ABSTRACT FROM AUTHOR]

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 R 2 score of 94.4%. Adding more PMUs does not provide any additional value in terms of accuracy. [ABSTRACT FROM AUTHOR]

The distribution network is a crucial component of the power system as it directly connects to users and serves the purpose of distributing power and balancing the load. With the integration of new energy sources through distributed generation (DG), the distribution network has undergone a transformation from a single power radial network into a complex multi-source network. Consequently, traditional fault location methods have proven inadequate in this new network structure. Therefore, the focus of this paper is to investigate fault location techniques specifically tailored for DG integration into distribution networks. This paper analyzes how fault conditions impact the characteristics of single-phase grounding faults and extracts spectral feature quantities to describe differences in zero-sequence currents under various fault distances. This paper also proposes a fault location method based on centroid frequency and a BPNN (back propagation neural network). The method uses centroid frequency to describe the features of zero-sequence currents; to simulate the mapping relationship between fault conditions and spectral features, BPNN is employed. The mapping relationship differs for different lines and distribution networks. When a line faults, the spectral features are calculated, along with the transition resistance and fault closing angle. The corresponding mapping relationship is then called upon to complete distance measurements. This location method can be applied to various types of distribution lines and fault conditions with high accuracy. Even with insufficient training samples, sample expansion can ensure accuracy in fault distance measurement. We built a distribution network consisting of four feeders with different types and lengths of each line on Simulink and verified the effectiveness of the proposed method by setting different fault conditions. The results suggest that the method has a clear advantage over other frequency domain-based approaches, especially for hybrid lines and feeder lines with branches in distribution networks. Additionally, the method achieves a measurement accuracy within a range of 100 m. [ABSTRACT FROM AUTHOR]

In distribution networks, time asynchrony exists between the phasor measuring unit (PMU) at both ends of a line, and the effective measurement time of the devices is short, leading to insufficient accuracy in phasor measurements. This paper proposes a fault location method for distribution networks that employ an additional inductance strategy to address the limited location accuracy caused by time asynchrony and the inadequate accuracy of phasor measurement devices. The method enhances the stability and accuracy of phase measurement by connecting an additional inductance after the online circuit breaker, thus extending the effective measurement time. It uses the symmetrical component method to obtain the positive-sequence and negative-sequence networks following a fault. Time asynchrony is treated as an equivalent asynchronous phase angle, which is then applied to the positive and negative-sequence voltage components. The impact of time asynchrony is mitigated by compensating for the phase angle difference using the ratio of the positive-sequence voltage component to the negative-sequence voltage component. This approach provides the fault location function and has improved the accuracy of fault location, which is advantageous for rapid fault repair. [ABSTRACT FROM AUTHOR]

Fast and accurate fault location in DC power systems is of particular importance to ensure their reliable operation. One of the approaches for implementing a fast‐tripping protection scheme is to use Traveling waves (TW) initiated by a fault scenario. This paper proposes a physics‐informed machine learning approach that utilizes TWs for fault location in DC microgrids. TWs are extracted by the so‐called multiresolution analysis which identifies the TW's wavelet coefficients for multiple frequency ranges. This paper deploys Parseval's theorem to find the energy of wavelet coefficients as a quantitative metric for describing TWs. The hypothesis of this paper is that once the Parseval energy curves for a specific cable are extracted, they can be utilized to locate faults along with that cable regardless of the DC system in which the cable is deployed. The fault location algorithm uses Parseval energy curves to train a Gaussian Process (GP) estimator. With the Parseval energy values of measured current at the protection device location, the GP estimator is able to estimate fault locations with high accuracy. The effectiveness of the proposed algorithm is verified by simulating a DC microgrid system in PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for detecting high-impedance faults (HIF) in distribution systems that uses the Hilbert transform. Our approach is based on determining the instantaneous frequency of signals and detecting deviations from a reference frequency. Our technique is very sensitive to fault fluctuations because it makes use of the Hilbert transform's ability to capture dynamic signal properties like phase and frequency alterations. This sensitivity enables the extraction of unique features that identify fault signals, providing critical insights into fault detection and location. Notably, our method is appropriate for the analysis of non-stationary signals, which are typical in power systems where signal attributes vary fast during fault conditions. Furthermore, our method resolves deviations by comparing them to a predefined range and displaying essential features such as basic frequency, RMS (Root Mean Square), Crest Factor, Minimum and Maximum Deviations, and Maximum Current Amplitude. These values offer unique insights into the present signal's qualities, which aids in defect detection and diagnostics, particularly in HIF settings. Our proposed technique detects high-impedance flaws by evaluating deviations from the nominal frequency, even in environments with weaker features and variable surface conditions. To improve our system's robustness and usefulness, we recommend performing additional study on adaptive thresholding algorithms and real-time implementation choices. Future research areas could involve investigating the integration of machine learning algorithms for automatic fault categorization and localization, which would enhance the capabilities of distribution system fault detection approaches. [ABSTRACT FROM AUTHOR]

The inevitability of faults arises due to prolonged exposure of photovoltaic (PV) power plants to intricate environmental conditions. Therefore, fault diagnosis of PV power plants is crucial to ensure the continuity and reliability of power generation. This paper proposes a fault diagnosis method that integrates PV power prediction and an exponentially weighted moving average (EWMA) control chart. This method predicts the PV power based on meteorological factors using the adaptive particle swarm algorithm-back propagation neural network (APSO-BPNN) model and takes its error from the actual value as a control quantity for the EWMA control chart. The EWMA control chart then monitors the error values to identify fault types. Finally, it is verified by comparison with the discrete rate (DR) analysis method. The results showed that the coefficient of determination of the prediction model of the proposed method reached 0.98. Although the DR analysis can evaluate the overall performance of the inverter and identify the faults, it often fails to point out the specific location of the faults accurately. In contrast, the EWMA control chart can monitor abnormal states such as open and short circuits and accurately locate the string where the fault occurs. [ABSTRACT FROM AUTHOR]

Fault detection and diagnosis of nonstationary processes are crucial for ensuring the safety of industrial production systems. However, the nonstationarity of process data poses multifaceted challenges to them. First, conventional stationary fault detection methods encounter difficulties in discerning evolving trends within nonstationary data. Secondly, the majority of current nonstationary fault detection methods directly extract features from all variables, rendering them susceptible to redundant interference. Moreover, nonstationary trends possess the capacity to conceal and modify the correlations among variables. Coupled with the smearing effect of faults, it is challenging to achieve accurate fault diagnosis. To address these challenges, this paper proposes sparse Wasserstein stationary subspace analysis (SWSSA). Specifically, a ℓ 2 , p -norm constraint is introduced to endow the stationary subspace model with excellent sparse representation capability. Furthermore, recognizing that fault variables within the sparse stationary subspace influence only a limited subset of stationary sources, this paper proposes a novel contribution analysis method based on local dynamic preserving projection (LDPP), termed LDPPBC, which can effectively mitigate the smearing effect on nonstationary fault diagnosis. LDPPBC establishes a LDPP matrix by extracting the latent positional information of fault variables within the stationary subspace. This allows LDPPBC to selectively analyze the contributions of variables within the latent fault subspace to achieve precise fault diagnosis while avoiding the interference of variable contributions from the fault-free subspace. Finally, the superiority of the proposed method is thoroughly validated through a numerical simulation, a continuous stirred tank reactor, and a real industrial roaster. • This method can realize fault diagnosis under the influence of nonstationarity. • The smearing effect of faults is effectively mitigated. • Through mathematical deduction, the convergence of the algorithm is proven. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence (AI), AI has been widely applied in anomaly analysis detection and fault location in power grid data and has made significant research progress. Through looking back on traditional methods and deep learning methods in anomaly analysis detection and fault location of power grid data, we aim to provide readers with a comprehensive understanding of the existing knowledge and research advancements in this field. Firstly, we introduce the importance of anomaly analysis detection and fault location in power grid data for the safety and stability of power system operations and review traditional methods for anomaly analysis detection and fault location in power grid data, analyzing their advantages and disadvantages. Next, the paper briefly introduces the concepts of commonly used deep learning models in this field and explores, in depth, the application of deep learning methods in anomaly analysis detection and fault location of power grid data, summarizes the current research progress, and highlights the advantages of deep learning over traditional methods. Finally, we summarize the current issues and challenges faced by deep learning in this field and provide an outlook on future research direction. [ABSTRACT FROM AUTHOR]

Cable fault location is generally much more difficult if cable defect has not reached the level of serious breakdown. Traditional methods such as time domain reflectometry (TDR) and partial discharge detection cannot solve this problem well due to their low sensitivity. This paper proposes a new non-destructive detection method based on the impedance spectroscopy (IS), which can effectively locate soft faults in the cable. In this paper, a numerical model for cable with distributed parameters is established firstly. Based on this model, cable faults are extracted from the imaginary part of impedance spectrum with Discrete Fourier Transform (DFT). The Chebyshev window is introduced to suppress interference and enhance signal-to-noise ratio, which improves the sensitivity and accuracy of fault location significantly. Experiments and simulations show that this method can well locate multi-segment defects in cables with good accuracy. The window function successfully amplifies the normalized amplitude of faults and improves the positioning accuracy. Compared with other method, this method reduces distortion points effectively. The fault location error is within 0.53%, which has great potential for field application. [ABSTRACT FROM AUTHOR]

Electrical utilities performance is measured by various indicators, of which the most important are very dependent on the interruption time after a failure in the network has occurred, such as SAIDI. Therefore, they are constantly looking for new techniques to decrease the fault location and repair times. A possibility to innovate in this field is to estimate the failed network component when a fault occurs. This paper presents the conclusion of an analysis carried out by the authors with the aim to estimate failure types of underground MV networks based on observable indirect variables. The variables needed to carry out the analysis must be available shortly after the failure occurrence, which is facilitated by a smart-grid infrastructure, to allow for a quick estimation. This paper uses the groundwork already carried out by the authors on ambient variables, historical variables, and disturbance recordings to design an estimator to predict between four MV cable network failure types. The paper presents relevant analyses on the design and performance of various machine learning classification algorithms for estimation of the types of MV cable network failures using real-world data. Optimization of performance was carried out, resulting in an estimator with an overall 68% accuracy rate. Accuracy rates of 94% for cable failure, 63% for excavations, and 79% secondary busbar failures were achieved; as for cable joints, the accuracy was poor due to the difficulty to identify a feature that can be used to separate this failure type from cable failures. Future work to improve that accuracy is discussed. [ABSTRACT FROM AUTHOR]

This paper presents a method for optimal reconfiguration of smart grids following the occurrence of short circuit faults. Due to restoration delays, the aim of the proposed approach is to save the maximum possible number of loads by forming stable islands and serving loads with Distributed Energy Resources (DERs). The islanding plan aims to prevent island instability and to help DERs continue supplying the maximum number of loads by the optimal network reconfiguration. Fault isolation is carried out by the protection system and the proposed procedure is commenced right after the fault isolation by controlling the condition of the network remote‐controlled switches. The proposed islanding plan is a novel method by this paper in the management of the postfault conditions of smart grids. Furthermore, a Q‐learning reinforcement approach is presented as the optimization tool because of its great capability and fast response for the determination of optimal reconfiguration. Numerous simulation tests for various fault locations on a 6‐bus and a 33‐bus test networks show the effectiveness of the proposed method in the improvement of postfault network reliability and sustainability. [ABSTRACT FROM AUTHOR]

A power line carrier (PLC) communication characteristics‐based method is proposed for single‐phase‐to‐ground fault location in neutral isolated medium voltage meshed distribution networks in this paper. The carrier signals with a time‐varying frequency and constant amplitude are processed by a set of PLC transmitters and receivers, whose placement is optimized by regarding the power network as an undirected graph. Two signal encoding and decoding algorithms for the PLC terminals are proposed to avoid using expensive timing systems between the terminals. The fault location technique is implemented by comparing the cosine similarity of amplitude attenuation and phase offset between the fault and a feature library. The node corresponding to the maximum cosine similarity of the characteristics between the present fault and the library is selected as the location of the current fault. Only one set of low‐cost PLC communication terminals and the widely available power lines are needed in the fault location system, making this approach highly practical. Numerical simulations using MATLAB/Simulink have been performed to verify the technique's feasibility. The results show that the method can accurately locate faults in neutral isolated medium voltage meshed distribution networks. Besides, the presented approach achieves a high level of accuracy in estimating transition resistance values. [ABSTRACT FROM AUTHOR]

The theme of the paper is to emphasize the detection and classification of faults and their site location in the transmission line using machine learning techniques which help to indemnify the foul-up of the humans in identifying the site and type of occurrence of fault. Moreover, the transient stability is a supreme one in power systems and so the disturbances like faults are required to be separated to preserve the transient stability. In general, the protection of the transmission line includes the installation of relays at both ends of the line that constantly monitor voltages and currents and operate unless a fault occurs on a line. Therefore, this paper intends to introduce a novel transmission line protection model by exploiting the hybrid optimization concept to train the Convolutional Neural Network (CNN). Here, the fault detection, classification and site location are diagnosed by using CNN which is trained and tested by making use of diverse synthetic field data derived from the simulation models of distinct types of transmission lines. Hence, the location and the type of faults will be predicted by the CNN depending on the fault signal characteristics which are optimally trained by a new hybrid algorithm named Chicken Swarm Insisted Spotted Hyena (CSI-SH) Algorithm that hybrids both the concept of Spotted Hyena Optimization (SHO) and Chicken Swarm Optimization (CSO). Finally, the proposed method based on CNN for fault classification and site location of transmission lines is implemented in MATLAB/Simulink and the performances are compared with various measures like classification accuracy, fault detection rate and so on. [ABSTRACT FROM AUTHOR]

A seismo-tectonic map depicting the principal structural elements of the northern Central American region has been meticulously crafted to characterize the tectonic setting and the individual seismo-tectonic structures of this area. This region is subject to heightened seismic activity, with a large number of medium-high Magnitude occurrences transpiring annually. This map is presented alongside an informative dataset wherein fault trace locations, geometry and kinematics descriptors and other available metadata have been stored. Therefore, the map offers a detailed and up-to-date depiction of the brittle deformation across the region, serving as a valuable resource for a comprehensive assessment of the seismo-tectonic framework. Moreover, the map and its accompanying database summarize fault characteristics for seismic hazard analysts and for civil protection workers, proving to be useful instruments in pinpointing areas where urgent fault research should be conducted from a seismic risk standpoint. [ABSTRACT FROM AUTHOR]

Earth faults is a challenging fault type to locate in resonant grounded networks due to their naturally low fault current, and the problem increases with an increased fault impedance. This paper describes the detailed implementation and laboratory testing of a method for detection, location and clearing of earth faults with very small fault currents. The method consists of two indicators used in the fault detection stage, where their simultaneous operation ensures selective fault detection and faulty feeder selection. One of these indicators also enables continuous fault indication throughout a sectionalizing process. The laboratory tests demonstrate that both indicators function as intended, and it is the current sensors which ultimately limit the attainable sensitivity. Faults up to 15 kΩ were detected successfully in the laboratory network based on phase current measurements, while the sectionalizing indicator showed much higher sensitivity and functioned as intended in a 50 kΩ fault. Measurements from one field test in a 22 kV network corroborate the laboratory results and demonstrate the expected earth fault indicator response. [ABSTRACT FROM AUTHOR]

Low and medium speed magnetic levitation traffic with short power supply distance and complex grounding network structure, prone to power supply rail grounding faults. However, the existing fault location methods do not accurately locate the fault point, making it difficult for the protective device to act to cut off the fault. To address the above problems, this paper builds a dynamic simulation model of the low and medium speed magnetic levitation power supply rails to study the distribution characteristics of the fault traveling waves after a ground fault occurs in the power supply rails, and analyses the generation mechanism of the traveling wave spectrum through formula calculation. First, the difference in current between the positive and negative bus bars is analyzed to determine whether a ground fault has occurred. Second, the direction of the current difference between stations is compared to locate the faulty section. Finally, the fault distance is calculated from the frequency difference of the fault voltage at the double‐ended station. Through simulation, the method is validated to be unaffected by fault location, fault transition resistance, noise interference, and is applicable to short circuit faults caused by lightning strikes, and the ranging error always remains within 20 m. The method has strong robustness, can effectively solve the problem of protection misoperation and accurately locate the fault point. It is suitable for low and medium speed magnetic levitation transportation power supply rail ground fault. [ABSTRACT FROM AUTHOR]

A new method for fault location during a single-phase-to-ground fault in the compensated distribution network is proposed in this paper. The method makes full use of the difference between transient-state fault information and the difference between steady-state fault information in two FTUs (feeder terminal units) of each section to determine faulty location. In this method, the transient-state component and steady-state component of zero-sequence current measured by each FTU are extracted by FastICA (fast independent component analysis) algorithm and are represented by the transient-state factor and steady-state factor, which are defined in this paper. Then, the difference between transient-state factors and the difference between steady-state factors in two FTUs of each section are calculated. The two differences corresponding to a certain section are seen as a feature vector, which is given as one input to the fuzzy C-means (FCM) algorithm. All feature vectors will be partitioned into two clusters, one of which represents a faulty section, and the other of which represents a healthy section. As a result, faulty location will be determined. Simulation results show the effectiveness and reliability of the proposed method under different fault conditions. [ABSTRACT FROM AUTHOR]

The inability to locate device faults quickly and accurately has become prominent due to the large number of communication devices and the complex structure of secondary circuit networks in smart substations. Traditional methods are less efficient when diagnosing secondary equipment faults in smart substations, and deep learning methods have poor portability, high learning sample costs, and often require retraining a model. Therefore, a secondary equipment fault diagnosis method based on a graph attention network is proposed in this paper. All fault events are automatically represented as graph-structured data based on the K-nearest neighbors (KNNs) algorithm in terms of the feature information exhibited by the corresponding detection nodes when equipment faults occur. Then, a fault diagnosis model is established based on the graph attention network. Finally, partial intervals of a 220 kV intelligent substation are taken as an example to compare the fault localization effect of different methods. The results show that the method proposed in this paper has the advantages of higher localization accuracy, lower learning cost, and better robustness than the traditional machine learning and deep learning methods. [ABSTRACT FROM AUTHOR]

The high resistance connection fault of the stator is a common fault in doubly fed induction generators, which causes a three-phase imbalance in the stator circuit. Since the stator winding is directly connected to the power grid, interference from the asymmetric power grid must be eliminated in order to achieve the accurate diagnosis of stator resistance imbalance faults. Therefore, a new diagnosis method based on filter shunt capacitor banks is proposed in this paper. By introducing shunt capacitor banks, an artificial neutral point is constructed to replace the neutral point of the power grid. Then, the neutral point voltage of the stator winding relative to the artificial neutral point is selected as a fault characteristic signal. In this paper, the change in three-phase stator currents after a high-resistance connection fault is analyzed in detail, and by comparing the fault characteristic signal with three-phase stator currents, the fault phase location and fault severity of high-resistance connection can be accurately obtained. Finally, simulations are carried out via the field-circuit coupling method to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Fault detection and location is one of the critical issues in engineering applications of modular multilevel converters (MMCs). At present, MMC fault diagnosis based on neural networks can only locate the open-circuit fault of a single submodule. To solve this problem, this paper proposes a fault detection and localization strategy based on a depthwise separable convolutional (DSC) neural network. By inputting the bridge arm circulating current and the submodule capacitor voltage into two serially connected neural networks, not only can this method achieve the classification of submodule open-circuit faults, submodule block short-circuit faults, and bridge arm inductance faults in MMCs, but it can also locate the switch where open-circuit faults occur. The simulation experimental results show that the proposed method achieves fault classification and locates multiple submodule open-circuit faults in the same bridge arm. This method achieves accuracies of ≥99% and 87.7% for the single-point and multi-point open-circuit fault localization in MMCs, respectively, which is better than some benchmark achievements in the current literature in terms of detection accuracy, and speed, and it has fewer model parameters and better real-time performance. [ABSTRACT FROM AUTHOR]

Promoted by the rapid development of information technology, 10kV overhead line has been widely used in the majority of cities, and it is of great significance to monitor the distribution network effectively, in order to ensure the normal operation of the system. Most of traditional distribution network monitoring methods are based on manual work, which causes inconvenience to the distribution network fault location, repair, maintenance and real-time monitoring, and reduces the efficiency of the distribution network emergency repair and the reliability of power supply. Aiming at the automatic monitoring problem of 10kV overhead network, this paper adopts an intelligent wireless monitoring technology, where a monitoring node is employed to monitor the network transmission status through wireless links. We evaluate the system monitoring performance by using the metric of outage probability, depending on the wireless data rate over wireless channels. For the considered system, we derive analytical outage probability, in order to measure the system performance in the whole range of signal-to-noise ratio (SNR). The simulation results are finally presented to verify the analytical expressions on the system monitoring outage probability in this paper. [ABSTRACT FROM AUTHOR]

Environmental concerns and economic constraints have led to increasing installations of mixed conductor circuits comprising underground cables (UGCs) and overhead transmission lines (OHLs). Faults on the OHL sections of such circuits are usually temporary, while there is a higher probability that faults on UGC sections are permanent. To maintain power system reliability and security, auto-reclose (AR) schemes are typically implemented to minimize outage duration after temporary OHL faults while blocking AR for UGC faults to prevent equipment damage. AR of a hybrid UCG–OHL transmission line, therefore, requires effective identification of the faulty section. However, the different electrical characteristics of UGC and OHL sections present significant challenges to existing protection and fault location methods. This paper presents a selective AR scheme for mixed conductor circuits based on the evaluation of differential currents in multiple defined protection zones, using distributed current transformer (CT) measurements provided by passive optical sensing. Case studies are conducted with a number of different UGC–OHL configurations, and the results demonstrate that the proposed scheme can accurately identify the faulty section, enabling effective selective AR of a comprehensive range of mixed conductor circuit topologies. The proposed scheme is also more cost effective, with reduced hardware requirements compared to conventional solutions. This paper thereby validates the optimal solution for mixed circuit protection as described in CIGRE Working Group B5.23 report 587. [ABSTRACT FROM AUTHOR]

In the dual inverter topology of open‐winding permanent magnet synchronous motor system, due to the symmetry of the topology, switch devices have high similarity in working characteristics, which undoubtedly increases the difficulty in locating faulty devices. In this paper, by analysing the difference of inverter output characteristics before and after switch devices open‐circuit fault, the relationship between the inverter output voltage error and fault location is found, and a fault diagnosis strategy is proposed. In this strategy, a construction method of fault phase characteristic current and the estimation equation of inverter output‐voltage error are designed. Then, the real‐time diagnosis of single device fault and two devices with the same characteristics fault at the same time is realized. Since the strategy proposed in this paper does not need additional voltage sensors, it has the same applicability as the traditional current method. In addition, the diagnosis method based on voltage information reduces the diagnosis time. Besides, this method is independent of the PWM strategy and is not sensitive to the phenomena of dead time and delayed insulated gate bipolar transistors switching. Finally, experimental results demonstrate the effectiveness of the proposed fault diagnosis strategy. [ABSTRACT FROM AUTHOR]

Electric power systems constantly encounter disturbances and faults, necessitating fast and precise identification and rectification of these issues. This is crucial for ensuring the stability and reliability of the system. This paper introduces a protection scheme for accelerating the second zone operation of the distance relay during internal faults. The proposed scheme exploits the locus of power with positive power characteristics to effectively distinguish between internal and external faults. This is achieved by detecting the remote circuit breaker operation (RCBO). The locus of power remains predominantly within regions 1 or 2, with occasional transfers between these regions due to internal faults prior to and following the RCBO. Conversely, in the case of external faults, regions 3 or 4 are implicated. This distinct variation in the locus of power is applied to derive the protection algorithm. This is affirmed through sequence network analysis of various faults in the transmission line. The cumulative rate of change in relative reactive power has been employed for single‐phase RCBO detection. The proposed protection logic employs supplementary undervoltage logic to avoid single‐phase operation during two‐phase and three‐phase faults. The simulations are conducted with meticulous consideration of key factors, such as fault type, fault resistance, fault location, fault inception angle, and power source angle. Simulation results demonstrate the effectiveness of the proposed protection scheme. [ABSTRACT FROM AUTHOR]

For the permanent magnet synchronous generator (PMSG) integrated into the flexible interconnected distribution network (FIDN), its low-voltage ride-through (LVRT) strategy needs to be designed to enhance the transient operation capability of the FIDN. The design of the LVRT strategy also needs to take the fault location of the FIDN into consideration. To deal with faults occurring on the integrated feeder of the PMSG, the PMSG needs to realize successful LVRT using the hardware protection equipment. To deal with faults occurring on the feeder adjacent to the integrated feeder of the PMSG, the PMSG needs to release its stored energy to temporarily increase its active power output, which is then supplied to the loads on the faulted feeder that are isolated from the fault through the soft open point (SOP). In this paper, a novel LVRT strategy designed for the PMSG integrated into the FIDN is proposed, which includes dual operation modes that are separately applied to different fault locations. If PMSG is on the faulted feeder, the DC-link voltage of the PMSG is maintained with the controllable resistive fault current limiter (CRFCL), while the maximized stored kinetic energy is reserved to enhance the generation efficiency during the LVRT. If PMSG is on the feeder adjacent to the faulted feeder, the control strategy of the converters of the PMSG is adjusted in response to the power regulation goal at the SOP. Meanwhile, the maximum releasable kinetic energy of the PMSG is considered when increasing its active power output. The feasibility and effectiveness of the LVRT strategy for the PMSG are verified based on the numerical analysis. [ABSTRACT FROM AUTHOR]

Transmission is an important aspect regarding an effective designing of electric supply system. Ensuring reliable and fault-free transmission from the source for effective distribution to the end consumers is very much desirable. In this respect, fast and accurate fault detection, particularly in the overhead transmission lines, is very pertinent. Various algorithms and novel approaches have been formulated by various researchers aligned to this challenge. In this context, a new algorithm influenced by the biotic procedure of flight skills of hummingbird seems to be one of the best algorithms to address the cited problem. This paper focuses on the formulation of this Artificial Hummingbird Algorithm (AHA) and its high accuracy in ameliorating the fault location in transmission line. The most common flight skills being used in the algorithm are foraging schemes, which includes axial, diagonal, and omnidirectional flights. The proposed AHA has been tested using the Simulink prototype in MATLAB for an overhead transmission line having a length of 300 km and system voltage of 400 kV at suitable lengths. Specimen signal of voltages and currents waveforms has been taken at duo ends of the overhead transmission line. The results of the proposed algorithm have been compared with the results obtained from previous studies, and it has been observed that this algorithm yields better results for various kinds of asymmetrical and symmetrical faults. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive exploration of the Advanced Impedance Mismatch Technique (AIMT), a novel approach designed for the accurate detection of simultaneous and varied faults within photovoltaic (PV) systems. This investigation integrates a spectrum of fault detection strategies, pinpointing reflectometry as a notably effective tool. Despite its utility, conventional reflectometry applications face critical constraints, notably the limitation to identify only the primary fault location within a PV array and the inability to distinguish between different fault types. This work introduces an innovative mathematical model that estimates the impedance of PV modules, enhancing the reflectometry method to enable the precise identification and localization of multiple defective modules within a string. The proposed technique exhibits a remarkable sensitivity to detect slight impedance differences between a functional PV string and one with defective modules. The validity of the AIMT's mathematical model is corroborated through simulation experiments on a string of seven PV modules afflicted with multiple simultaneous faults. These experiments rigorously evaluate the technique's accuracy in pinpointing the locations of defective modules within a PV string. The outcomes of our proposed N $N$‐times faster AIMT reveal a strong concordance between the simulated reflective signals and the impedance values forecasted by the model, highlighting the proposed method's proficiency in the detailed detection and diagnosis of progressive faults within PV systems. [ABSTRACT FROM AUTHOR]

Machine learned fault detection approaches are being increasingly used for fault detection in distribution grid. However, the performance of the models can be improved by customizing the models. In this regard, a customised artificial neural network (CANN) for fault detection in a distribution grid is proposed in this paper. The proposed work develops a CANN that combines the "up‐pyramid" and "down‐pyramid" model of ANN into a "custom‐pyramid" model. As a result, the same model can be used both for determining the types of fault as well as its location. The data needed to train the model has been taken from a reconfigured IEEE‐33 bus distribution system developed in Typhoon HIL real‐time simulator. Spectral‐kurtosis is utilized for extraction of features of the faulted transient signals which are used as input data to develop the CANN. The result showcases that the reduction of input features reduces computational complexity without compromising its accuracy. The proposed model classifies fault location with an accuracy of 95.43%. The proposed method also identifies fault type with an accuracy of 96.08%. Several test cases have been developed to test the method. The method proved to be able to perform in most of the cases. [ABSTRACT FROM AUTHOR]

The integration of Distributed Energy Resources (DERs) in power distribution networks poses challenges for protection systems due to dynamic bidirectional fault currents. This paper presents a novel wide-area protection scheme for modern Doubly Feed Induction Generator (DFIG)-integrated distribution networks that simplifies fault location and relay coordination. The system employs Artificial Neural Networks (ANNs) for fault detection and a Fault Zone Relay Condition (FZRC) matrix for fault location. The simulation results demonstrate the effectiveness of the proposed approach in fault detection and isolation. [ABSTRACT FROM AUTHOR]

Ensuring the safe and stable operation of high-speed trains necessitates real-time monitoring and diagnostics of their suspension systems. While machine learning technology is widely employed for industrial equipment fault diagnosis, its effective application relies on the availability of a large dataset with annotated fault data for model training. However, in practice, the availability of informational data samples is often insufficient, with most of them being unlabeled. The challenge arises when traditional machine learning methods encounter a scarcity of training data, leading to overfitting due to limited information. To address this issue, this paper proposes a novel few-shot learning method for high-speed train fault diagnosis, incorporating sensor-perturbation injection and meta-confidence learning to improve detection accuracy. Experimental results demonstrate the superior performance of the proposed method, which introduces perturbations, compared to existing methods. The impact of perturbation effects and class numbers on fault detection is analyzed, confirming the effectiveness of our learning strategy. [ABSTRACT FROM AUTHOR]

Under a distributed information system, the scale of various operational components such as applications, operating systems, databases, servers, and networks is immense, with intricate access relationships. The silo effect of each professional is prominent, and the linkage mechanism is insufficient, making it difficult to locate the infrastructure components that cause exceptions under a particular application. Current research only plays a role in local scenarios, and its accuracy and generalization are still very limited. This paper proposes a novel fault location method based on dynamic operation maps and alarm common point analysis. During the fault period, various alarm entities are associated with dynamic operation maps, and alarm common points are obtained based on graph search addressing methods, covering deployment relationship common points, connection common points (physical and logical), and access flow common points. This method, compared with knowledge graph approaches, eliminates the complex process of knowledge graph construction, making it more concise and efficient. Furthermore, in contrast to indicator correlation analysis methods, this approach supplements with configuration correlation information, resulting in more precise positioning. Through practical validation, its fault hit rate exceeds 82%, which is significantly better than the existing main methods. [ABSTRACT FROM AUTHOR]

Power transmission lines in Ethiopia are experiencing an alarmingly high frequency of unexplained outages triggered by environmental factors, which significantly undermine the reliability of the country's power system. This paper presents investigations aiming to identify those among the unexplained fault records that have been caused by pollution induced flashovers. An identification method is developed, which associates the contextual fault features, such as information about the characteristics of the fault, fault location, and time of day, as well as month of its occurrence with local meteorological/climatic and environmental conditions. A total number of 4231 unexplained faults, recorded between 2015 and 2022, were analyzed. Among them, 1045 faults (24.7%) were identified as being most likely caused by pollution induced flashovers. The entire network suffered from more than 130 pollution-induced flashovers annually with a frequency of about 0.8 faults/100 km/year. The fault frequency strongly differed among the grid regions, being highest in the Northeast and lowest in the Southwest region. Moreover, the performed analyses also concentrated on the evaluation of the pollution performance of various insulator types employed in the network. The results indicate that porcelain insulators have the highest pollution-induced flashover intensity of 1.47 faults/year/1000 units, followed by silicone rubber polymeric composite insulators and glass insulators with the respective intensities of 1.21 and 0.83 faults/year/1000 units. These results indicate that despite the high expectations towards the pollution performance of silicone rubber polymeric insulators, their use in the Ethiopian climatic and environmental conditions appears to be unsatisfactory. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The reconfigurable capability of static random‐access memory (SRAM) field programmable gate array (FPGA) can be used for its fault self‐repair method. As a machine learning method, the genetic algorithm (GA) is an FPGA fault repair method that can be automatically executed on‐orbit without any ground support. However, the GA‐based fault repair method has disadvantages, such as the dependency on processors, the knowledge requirement for user designs in FPGAs, and the small size of repaired circuits. To address these issues, this paper presents a comprehensive analysis of the FPGA bitstream in the aerospace industry. An accurate on‐orbit fault location can be identified by bitstream copying and exhaustive test and the executed area of the GA can be reduced to one tile. In addition, the probability function of the algorithm is optimized, which converts floating‐point operations into integer arithmetic operations that are easily implemented in FPGAs without processors. The method is outstanding compared with existing ones, considering: (1) The size of repaired circuits is hundreds of times larger than those from other methods. (2) Its implementations are totally up to FPGAs' own logic, with no requirement for processors. (3) There is no knowledge requirement for user design. (4) It reaches the leading level with a success rate of 81%–93%. The method has been verified by various applications in XC7VX330T, which demonstrates its engineering practicability. [ABSTRACT FROM AUTHOR]

Aiming at the operation and maintenance requirements of the fault location of high-temperature superconducting cables, a fault location method of high-temperature superconducting cables based on the improved time-frequency domain reflection method and EEMD noise reduction is proposed. Considering the cross-term interference problem in the traditional time-frequency domain reflection method, this paper introduces the affine transformation to project the time-frequency distribution of the self-term and the cross term and further highlights the characteristic differences between the two through coordinate transformation, and the particle swarm algorithm is employed to solve the optimal stagger angle of the affine transformation. The unscented particle filter is adopted to separate the cross term, and EEMD noise reduction is introduced to solve the signal noise problem. Finally, two software programs, PSCAD and MATLAB, are employed for joint simulation to build a model of high-temperature superconducting cable. The simulation example shows that the proposed method in this paper can eliminate the cross-term interference of the traditional time-frequency domain reflection method, effectively locate the fault of the high-temperature superconducting cable, and improve the positioning accuracy. [ABSTRACT FROM AUTHOR]

Flexible DC distribution networks have been widely developed because of their advantages of high efficiency, low cost, and easy access to renewable energy. However, as distribution networks have more ends than the two-terminal transmission line, it is more challenging to protect them and locate their faults. Therefore, this paper proposes a modified T-source circuit breaker to protect against DC faults. Based on the proposed circuit breaker, an integral fault location method using double-terminal asynchronous data is proposed. The accuracy of the algorithm is not affected by the fault resistance. There are no differential terms in the algorithm, which greatly reduces the sensitivity to noise. The integral algorithm can remain steady when the voltage and current are zero in oscillation and attenuation periods. This constitutes the main contribution of this paper. Finally, it is verified by simulation and experiment that the proposed circuit breaker can isolate the power source within a short time in the typical DC distribution network. Under a noise of 30 dB for simulation, the location error of the proposed algorithm is <0.4% when the fault resistance is 0.5 Ω–500 Ω. [ABSTRACT FROM AUTHOR]

Compared with the pole-to-pole short circuit, the fault characteristics are not obvious when a monopolar grounding fault occurs in a DC distribution network, and it is difficult to locate the fault accurately. To solve this problem, this paper proposes a fault location method based on improved ReliefF and Weighted random forest (WRF). The 24 time and frequency-domain fault features of the postfault aerial mode current are calculated, and the most useful features are selected to form the optimal feature subset for input to the fault location estimator. In this paper, the ReliefF algorithm is utilized for automatic feature selection and obtaining the weights of features. In addition, the WRF algorithm is used to build the fault location estimator. Considering the fault location, fault resistance, noise and time window length, the Matlab/Simulink simulation platform is used to simulate the fault situation and compare it with other algorithms. The simulation results show that the average positioning error of the fault location method is less than 0.1%, which is not affected by the fault resistance and has strong robustness. [ABSTRACT FROM AUTHOR]

Weather-related or other types of faults can negatively impact the continuity of the electricity delivery to the consumers and, thus, lead to poorer reliability scores. 90% of customer outage-minutes are due to events which affect local distribution systems. The radial configuration is simple and has a low cost design; however, when a fault occurs it often causes power outages beyond the location of the fault itself. Therefore, it is beneficial for utilities to automatically isolate the fault and, thus, restore power to as many consumers as possible. The benefits of fault isolation and service restoration include, but are not limited to: enhanced reliability and resiliency, reduced loss of revenue to the utility, and enhanced comfort and satisfaction for the utility’s consumers. This paper provides an algorithm to quickly isolate a fault and automatically restore power to as many nodes of the distribution system as possible by opening and closing switches already placed within the system. The expert model and centralized algorithm that are proposed in this paper guarantee an optimal solution and a short run-time regardless of the size of the distribution system. A mathematical proof of the optimal nature of the solution is provided. In addition, the model is validated in practice, solving two different-sized systems. [ABSTRACT FROM AUTHOR]

The traditional medium-voltage distribution network fault location method uses mainly the voltage and current measurements of the medium-voltage side, which results in problems such as high installation costs at the measuring points and complicated postoperation and maintenance work. Therefore, a fault location idea based on the distributed measurement of low-voltage side voltage is proposed in this paper. First, the characteristic voltage is adaptively selected according to the fault type. Second, the suspected fault section is determined by comparing the characteristic voltage amplitude of each measuring point. Third, the fault section is located using the section unit characteristic voltage drop defined for each suspected fault section. Finally, fault distance estimation is achieved based on the voltage difference matrix and characteristic voltage analysis. This method achieves accurate fault distance identification based on the distribution difference of the characteristic voltage of the low-voltage side under the fault state. This work provides a new economical and practical idea for determining the fault locations of distribution networks. The effectiveness of this method is evaluated by considering a 10 kV distribution network in Guangdong Province built in PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

The paper presents a ground fault location algorithm (GFLA) in double‐circuit transmission lines (DCTL) with a T‐off connection to an industrial microgrid (IMG) containing electric arc furnaces (EAF), renewable distributed generations (RDG), and static VAR compensator (SVC). The design presented utilizes the information on the current and voltage phasors of one side of the transmission lines (TL) during the fault period, and there is no need for the availability of information on the type of faulty phase(s) and even the faulty section. To find the exact location of the fault in this design, the voltage phasor equations governing the fault point in positive, negative, and zero‐sequence (PNZS) domains, the zero‐sequence current (ZSC) equation of the fault point, and the boundary condition equation of the fault point have been used. The simulation system is implemented in Simulink/MATLAB using accurate modelling of all network components. The algorithm has been tested and evaluated in different fault conditions, including various locations, phase types, phase‐to‐ground connection resistance, occurrence time, and microgrid (MG) operation modes. The successful results of various tests of the proposed design as well as the analysis of various sensitivities confirm the satisfying performance of the suggested algorithm. [ABSTRACT FROM AUTHOR]

Every power system is vulnerable to fault occurrences. Under permanent fault conditions, maintenance crew has the duty to detect the problem, repair the defect and recover the power supply system. If the fault is previously located, the repair can be performed faster. The common methods to locate the fault in electrical distribution systems use the final user information or some heuristics with fuse coordination and the loss of loads. In this paper, a new algorithm for fault location in distribution power systems is presented. Using computational simulations, travelling waves theory, wavelet transform, a new data preprocessing feature, and artificial neural networks, this new algorithm tries to approximate the fault location using data provided by only one measurement point at the beginning of the feeder. [ABSTRACT FROM AUTHOR]