Topology-Adaptive ground fault location method for distribution networks

PurposeAn electrical power distribution network is expected to deliver uninterrupted power supply to the customers. The disruption in power supply occurs whenever there is a fault in the system. Therefore, fast fault detection and its precise location are necessary to restore the power supply. Several techniques are proposed in the past for fault location in distribution network but they have limitations as their fault location accuracy depends on system conditions. The purpose of this paper is to present a travelling wave-based fault location method, which is fast, accurate and independent of system conditions.Design/methodology/approachThis paper proposes an effective method for fault detection, classification and location using wavelet analysis of travelling waves for a multilateral distribution network embedded with distributed generation (DG) and electric vehicle (EV) charging load. The wavelet energy entropy (WEE) is used for fault detection and classification purpose, and wavelet modulus maxima (WMM) of aerial mode component is used for faulted lateral identification and exact fault location.FindingsThe proposed method effectively detects and classifies the faults, and accurately determines the exact fault location in a multilateral distribution network. It is also found that the proposed method is robust and its accuracy is not affected by the presence of distributed generation and electric vehicle charging load in the system.Originality/valueTravelling wave based method for fault location is implemented for a multilateral distribution network containing distributed generation and electric vehicle load. For the first time, a fault location method is tested in the presence of EV charging load in distribution network.

The precise fault location is still an unsolved problem in distribution networks. The application of distributed generation (DG) increases the difficulty of fault location. The fault location in distribution network with DG has been becoming a hot and difficult subject. This paper proposes an impedance-based fault location method for distribution network with DG. First, the impedance models are built. The fault characteristics of the distribution network with DG are analyzed and extracted based on the impedance model. Then the fault section location technology is realized by using the impedance model and the fault characteristics. At last, the differential evolution algorithm is used to precisely locate fault in distribution network with DG. In the algorithm, the variables are the fault distance and the grounded resistance, and the fitness function is the value of the fault characteristic. The differential evolution algorithm can quickly search the precise fault location. The simulation results show that the impedance model of the system, the fault section location method and the fault location method are all effective.

The research on fast and accurate fault location technology in distribution networks is of great significance. Currently, many methods for fault location in distribution networks have been proposed, but most theories are difficult to solve the problem of fault location in multi branch distribution networks. Therefore, this paper analyzed the characteristics of traveling wave transmission and proposed a discrimination algorithm that uses the polarity of traveling waves to determine the source of fault traveling waves; Then, using the idea of first locating the fault section and then locating the fault point, a multi branch distribution network traveling wave fault location algorithm is proposed based on the configuration of micro PMU (Phasor Measurement Units) devices in the distribution network by combining the two end method and single end method of traveling wave distance measurement. A multi branch distribution network simulation model based on IEEE14 node standard topology was built using MATLAB/Simulink software, the experimental results also showed the effectiveness and the efficiency of the proposed algorithm.

As more and more DGs plugged into low voltage side of the distribution network, the low voltage distribution network has been changed to active distribution network. Bidirectional power flow and uncertainty of DGs' output cause the conventional fault location methods to be inapplicable in active distribution network for malfunction and miss trip, which makes the fault location in low voltage active distribution network an urgent problem. In order to solve this problem, a novel fault location and recognition method for low voltage active distribution network which needs 3-phase currents measurement only is proposed in this paper. In the proposed method, a low voltage active distribution network is installed with the measurement and data process units for current measurement and data process at nodes and a control center for fault location and recognition. By the division of the low voltage active distribution network into several two-terminal sections without branch, the fault location (inside/outside the section) with fault-phase selection can be determined by the calculation of A, B and C phase current phase-angle differences of each section and the fault type can be identified by judging the amplitudes of zero-sequence node currents at both ends of every fault section. Simulation verification of the proposed method has been carried out by MATLAB/Simulink, the simulation results indicate that fault in low voltage active distribution network can be located and recognized accurately by the proposed method with fast speed and self-adaption.

Accurate fault location of distribution network enables the fault to be quickly found and eliminated. It saves the manpower and material resources of the line inspection, reduces the losses caused by power outages, and ensures the safe and stable operation of the system. The traditional fault location method in distribution network uses the amplitude of voltage and current to calculate the fault distance without using the phase angle information. The distribution network operates in a variety of ways with fewer observation points. The actual line parameters of the system are inconsistent with the simulation parameters. At the same time, there is the access of distributed generation. All these make it is difficult to locate faults accurately in traditional distribution networks. With the application of the Micro Synchronous Phasor Measurement Unit (μPMU), phase angle information can also be used for fault location. Based on Stack Auto-encoder (SAE), in this paper, the voltage and current amplitude, and phase angle of the double-ended μPMU are used to train the SAE to calculate the fault distance. Line parameters, ground resistance and fault type are changed during training. A simulation model is built in PSCAD. The results show that this method is effective in fault location and robust against the effects of ground resistance and fault type. The effect of fault location will be very poor if only the amplitude of voltage and current is used.

One of the main factors that disrupt reliability and stop energy provision is the fault occurrence in distribution networks. Thus, accurate and fast fault prediction and location in distribution networks are essential for increasing reliability, fast restoration, optimal electrical energy consumption, and customer satisfaction. This study reviews and investigates fault prediction and fault location topics. To this end, the existing methods and views in the context of fault prediction are reviewed first; then, fault location is investigated. This paper investigates various methods, their advantages, disadvantages, technical reports, and patents in conventional distribution networks, smart-grids, and micro-grids. Comparison of this study with other surveys indicates that it is more comprehensive and despite others covers fault prediction. In addition, it includes an up to date review of the methods for distance measurement and fault location considering different network types (AC/DC), presence of DG, communication and automation standards, synchronous and unsynchronous measurement, magnetic measurement, and state estimation-based fault location methods.

Background: At present, small current grounding system has been widely used in distribution network of China. Affected by the complex topology of the distribution network and other factors, single-phase grounding fault has become the most prone type of electrical short-circuit fault in China. Objective: Considering that the traditional fault selection and location methods are difficult to mine the effective information of fault quantity, a new method is proposed in this paper to achieve accurate fault location on the basis of ensuring timeliness. Methods: In this paper, the physical topology of the distribution network is regarded as a graph, the overhead lines and cables of the main equipment are regarded as the nodes in the graph, and the problem of fault node location is corresponding to the task of graph attention classification. Considering the average degree and homogeneity of the given network topology, an improved graph attention network is built to realize fault node location. Results: This paper verifies the effectiveness of the proposed model for fault location through simulation in PSCADA. In addition, the applicability of the proposed model in the case of changes in the distribution network structure is verified. It verifies that the proposed method achieves high positioning accuracy.

Fault location in distribution networks is a major challenge that needs to be addressed in power distribution systems. Currently, fault location methods based on matrix algorithms, genetic algorithms, deep learning, and other algorithms have received wide attention from the industry. However, such methods have some drawbacks: (1) they require high accuracy in fault information uploads, which can lead to low fault tolerance; (2) they tend to converge early and get stuck in local optimal solutions; (3) they involve high computational complexity, leading to location delays. In this study, we propose a fault location framework based on recurrent neural network (RNN) and transfer learning. In this method, we first encode the information data collected from distribution terminals, and then use RNN to establish a nonlinear mapping relationship between fault features and fault location intervals, which effectively improves fault tolerance and reduces misjudgment issues. We then use transfer learning to load the pre-trained model onto the target task to address the problem of insufficient data for fault location in distribution networks. Experimental results show that after 15 rounds of training, our T-RNN model has achieved over 80% accuracy. Benefiting from Glorot weight initialization adopted after transfer learning, the model achieves good performance early on compared to the BP model, converges faster, and ultimately achieves a prediction accuracy of 96.5%.

The fault location of an active distribution network is a vital analysis to prevent major outages in the power system. Considering the influence of renewable distributed generations on fault characteristics, this paper proposes a novel location method based on a dynamic quantum genetic algorithm to solve for fault locations in active distribution networks. In the method, the fault current code is measured based on feeder terminal units. A universal switching function is presented to convert the feeder switch status into an uploaded fault current code. The fault location model is defined as an optimization problem that presents the evaluation objective function with an anti-false-positive factor. The dynamic quantum genetic algorithm is developed to locate the fault feeder according to the uploaded fault current code of the feeder terminal unit. The algorithm adopts dynamic rotating gate strategy and adaptive quantum crossover strategy to satisfy the requirements of quickness and accuracy for fault location. Moreover, the method avoids easily falling into a local optimum by integrating the discrete quantum mutation. The proposed fault location technique is tested and compared to other existing techniques on a 33-bus active distribution network. The simulation results show that the proposed fault location method can locate fault feeders accurately with fast computational times under conditions of single or multiple faults and with an information distortion of the feeder terminal unit.

On the basis of analysis on difficulty of a single phase grounding fault locating in distribution network, a fault location method using C-type of traveling wave is put forward. The C-type of traveling wave location method is off-line. This location method can not be affected by the signal intensity of traveling wave when the fault is taking place and would be used repeatedly. As a result, the measure accuracy is guaranteed. Through the simulation to traveling wave’s process of reflection and refraction by ATP, the result what simulation experiment shows by putting up model of 10kV line with 3 branches single phase grounding fault, we can compare waveform of the normal phase and the fault phase to find the first waveform distortion point and determine the fault location. The fault location precision meets accuracy requirements, so it is feasible to locate the fault by using C-type of traveling wave location method.

Fault voltage and fault current have nonlinear and timing characteristics. To deeply study the importance of each characteristic variable for fault line selection and fault location of the distribution network, a fault line selection and fault location method based on a random forest (RF) algorithm was proposed. The location function is completed simultaneously based on line selection by improving the random forest sub-model. The proposed network amplifies the single label system to the multi-label task network, which facilitates the better expression of fault data features, improves the feature extraction ability of the network, and dramatically reduces the time of fault diagnosis. In this study, the fault data obtained by Simulink simulation is used as the training set, and the RF model is established based on the Scikit-Learn framework. The results show that this model has a high fault line selection rate and small location error. It can be used as an auxiliary means of distribution network fault diagnosis.

With the increasing attention to the power supply reliability of distribution network, the fault location method of distribution network as an important subject can be studied in depth. Traditional fault location methods, such as impedance method and traveling wave method, are limited due to the complex topology and many line types of distribution network. Based on the analysis of the limitations of impedance method and traveling wave method, this paper studies the fault location method of distribution network based on time reversal, focusing on the influence of hybrid power network and load on fault location method, Compared with the traditional fault location method, the distribution network fault location method based on time reversal has stronger adaptability.

This paper develops a novel graph convolutional network (GCN) framework for\nfault location in power distribution networks. The proposed approach integrates\nmultiple measurements at different buses while taking system topology into\naccount. The effectiveness of the GCN model is corroborated by the IEEE 123 bus\nbenchmark system. Simulation results show that the GCN model significantly\noutperforms other widely-used machine learning schemes with very high fault\nlocation accuracy. In addition, the proposed approach is robust to measurement\nnoise and data loss errors. Data visualization results of two competing neural\nnetworks are presented to explore the mechanism of GCN's superior performance.\nA data augmentation procedure is proposed to increase the robustness of the\nmodel under various levels of noise and data loss errors. Further experiments\nshow that the model can adapt to topology changes of distribution networks and\nperform well with a limited number of measured buses.\n

This paper proposed a new electrical synaptic transmission-based Spiking Neural P system (SNP system) based on SNP systems. Some new elements are added into the original definition of SNP systems, such as new synapses, bidirectional model, two types of neurons and cancelling the delay of axon. Because SNP system is easy to express the logical relationship between graphics and has strong ability to process information in parallel, the bidirectional characteristics of electrical synaptic transmission is effectively combined with the electrical quantity (direction of current) for fault location of distribution network with distributed generations (DGs) in this paper. The fault location model and reasoning algorithm of the electrical synaptic transmission-based SNP system are studied with the advantages of high accuracy, less computation, simple and intuitive model and reasoning. Furthermore, the algorithm is applied reasonably in the bidirectional power flow characteristics of a distribution network with DGs. Finally, this paper verifies the effectiveness, accuracy and reliability of the method through two cases which involve the single fault, multiple fault and misinformation fault. With the large-scale DGs (distributed generation) access to the distribution network, which changes the power flow structure and operation mode, the traditional protection strategy of the passive distribution network is no longer applicable. The fault location problem of distribution network with DGs is carried out. Firstly, Many fault location methods in the existing literatures have been anaylsised in this paper, then the electrical synaptic transmission-based spiking neural P system (ESSNP) has been proposed to solve the fault location problem. Foremore, the fault diagnosis mode for the power outage section with ESSNP has been established, which construct with Network description matrix and Fault current matrix. In order to verify the accuracy of the method in this paper, three cases have been studied, which the single fault, multiple faults and misinformation faults in each case. Finally, conclusions have been discussed.

In view of the large scale distributed power distribution network, distribution network from the traditional single main transformer power supply system becomes more complex broken power supply network, the trend of the distribution network flow and network frame produced change, failure fault feature information of great change, the traditional distribution network fault location method can not accurately obtain the location of the fault zone, the low accuracy of fault location, the error bigger problem a new fault location method of active distribution network combining graph theory and matrix was proposed. According to the knowledge of graph theory, the distribution network is simplified to topology diagram and described in the form of matrix, and then the fault judgment matrix is obtained through a series of matrix addition operation, and the type of fault interval can be identified accurately and quickly by combining the fault criterion table. The simulation results show that this method is simple in principle, fast and effective in criterion, suitable for the flexible operation mode of active distribution network structure, can accurately and quickly determine the fault segment, and can meet the requirements of complex distribution network fault location.