Fault Line Selection Research Articles

Research on Fault Line Selection Method Based on Convolutional Neural Networks

In order to improve the reliability and accuracy of Single-Phase-to-Ground Fault line selection for small current grounding systems, this paper proposes a method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses the original current signal as input and uses a wide kernel in the first convolutional layer to extract features and suppress high-frequency noise. The following convolutional layers with small convolution kernels are used for multilayer nonlinear mapping. Batch normalization layers and dropout layers are carried out for improving the generalization ability of the model. The simulation results show that the recognition rate of the proposed WDCNN can reach 99%, and it is not affected by factors such as grounding impedance, fault location and fault time.

Fault line selection in\xa0cooperation with multi-mode grounding control for the\xa0floating nuclear power plant grid

The Floating nuclear power plant grid is composed of power generation, in-station power supply and external power delivery. To ensure the safety of the nuclear island, the in-station system adopts a special power supply mode, while the external power supply needs to be adapted to different types of external systems. Because of frequent single phase-ground faults and various fault forms, the fault line selection protection should be accurate, sensitive and adaptive. This paper presents a fault line selection method in cooperation with multi-mode grounding control. Based on the maximum united energy entropy ratio (MUEER), the optimal wavelet basis function and decomposition scale are adaptively chosen, while the fault line is selected by wavelet transform modulus maxima (WTMM). For high-impedance faults (HIFs), to enlarge the fault feature, the system grounding mode can be switched by the multi-mode grounding control. Based on the characteristic of HIFs, the fault line can be selected by comparing phase differences of zero-sequence current mutation and fault phase voltage mutation before and after the fault. Simulation results using MATLAB/Simulink show the effectiveness of the proposed method in solving the protection problems.

Neutral Grounding Fault Line Selection Algorithms for Low Voltage Side of Distribution Transformers in Civil Buildings

The neutral grounding fault line selection of low-voltage side of distribution transformer in civil buildings is of great significance in the field of power system. It is necessary to analyze the grounding fault of distribution transformer in civil buildings, calculate the current energy of distribution transformer by using the frequency coefficient of wavelet packet decomposition, and analyze the current fault components of grounding fault phase and sound phase, get the energy ratio of grounding fault current component, and complete the grounding fault of distribution transformer. Experiments show that the proposed algorithm can reasonably judge the fault line of distribution transformer nodes, and has a high accuracy in the selection of grounding faults.

Optimal Denoising and Feature Extraction Methods Using Modified CEEMD Combined with Duffing System and Their Applications in Fault Line Selection of Non-Solid-Earthed Network

As the non-solid-earthed network fails, the zero-sequence current of each line is highly non-stationary, and the noise component is serious. This paper proposes a fault line selection method based on modified complementary ensemble empirical mode decomposition (MCEEMD) and the Duffing system. Here, based on generalized composite multiscale permutation entropy (GCMPE) and support vector machine (SVM) for signal randomness detection, the complementary ensemble empirical mode decomposition is modified. The MCEEMD algorithm has good adaptability, and it can restrain the modal aliasing of empirical mode decomposition (EMD) at a certain level. The Duffing system is highly sensitive when the frequency of the external force signal is the same as that of the internal force signal. For automatically identifying chaotic characteristics, by using the texture features of the phase diagram, the method can quickly obtain the numerical criterion of the chaotic nature. Firstly, the zero-sequence current is decomposed into a series of intrinsic mode functions (IMF) to complete the first noise-reduction. Then an optimized smooth denoising model is established to select optimal IMF for signal reconstruction, which can complete the second noise-reduction. Finally, the reconstructed signal is put into the Duffing system. The trisection symmetry phase estimation is used to determine the relative phase of the detection signal. The faulty line in the non-solid-earthed network is selected with the diagram outputted by the Duffing system.

Fault Line Selection Method for Distribution Network Based on Angle Similarity

Aiming at the problem that the current distribution network protection technology has a low accuracy of line selection, the method of fault line selection based on angle similarity is proposed. A plurality of fault feature quantities of each feeder in different operating states are collected to form a historical database. The clustering centers of fault classes and non-fault classes are obtained by fuzzy clustering analysis. Similarly, the real-time data of each feeder is collected to form a sample of the feature to be tested. The degree of similarity between the feature samples to be tested and the historical feature samples is analyzed by angle similarity. The sample to be tested is classified into a fault class or a non-fault class, and finally the fault feeder is accurately selected.

Research on Line Selection Method of Single-phase Earth Fault Based on Improved Dynamic Time Bending Distance Similarity Word

The problem of fault line selection for the distribution network of neutral point via arc suppression coil grounding has not been solved for a long time. Based on this, a single-phase ground fault line selection method based on dynamic time bending distance similarity is proposed. Firstly, the single-phase ground fault feature analysis of the distribution network of the zero-mode network is used to determine the electrical characteristic quantity of the single-phase ground fault line selection. Then, the faulty electrical feature quantity is improved by the dynamic time bending distance algorithm to form the distance matrix. The similarity data processing is performed on the matrix to obtain a line selection vector, and then the fault line is selected. Finally, the single-phase ground fault system model of 10kV distribution network is constructed by Matlab/Simulink. The correctness and feasibility of single-phase ground fault line selection method based on the dynamic time bending distance similarity is verified by simulation under different fault conditions.

Series Arc Fault Diagnosis and Line Selection Method Based on Recurrent Neural Network

Series arc fault is a common phenomenon in the power system, it will directly affect the working reliability, but there is no mature method to detect it due to its concealment and chaos. Common detection methods that build on the arc fault eigenvectors obtained by manual analysis are subjective and incomprehensive. A series arc fault diagnosis and line selection method based on recurrent neural network (RNN) for a multi-load system was proposed in this paper. Firstly, a series arc fault experiment under a multi-load system was carried out, the training set and test set were built by using the data obtained from the experiment. Then, the RNN model was built, trained, and tested through the training set and test set. Finally, the fast-continuous detection method and the probability-based classification result correction method were proposed, and the detection speed and accuracy were improved much further. The results show that the proposed method is effective for diagnosing series arc fault and line selection under a multi-load system, without analysis of arc fault characteristics.

Two-Terminal Fault Location Method of Distribution Network Based on Adaptive Convolution Neural Network

When a single-phase ground fault occurs in a distribution network, it is generally allowed to operate with faults for one to two hours, which may lead to further development of the fault and even threaten the safe operation of the power system. Therefore, when a small current system has a ground fault, it must be quickly diagnosed to shorten the time of operation with fault. In this paper, an adaptive convolutional neural network (ACNN)-based fault line selection method is proposed for a distribution network. This method improves the feature extraction ability of the network by improving the pooling model. Compared with deep belief network (DBN), it can improve the accuracy of fault classification by 7.86% and reduce the training time by 42.7%. On this basis, the secondary fault location is identified using the principle of two-terminal fault location. In this research, fault data obtained by Simulink simulation is used as training set, and ACNN model is built based on TensorFlow framework. The analysis of results proves that the model has a high fault recognition rate and fast convergence speed. It can be used as an auxiliary hand for fault diagnosis in distribution networks.

Detection and Line Selection of Series Arc Fault in Multi-Load Circuit

In order to study a kind of detection and line selection method of arc fault in actual power supply and distribution lines, arc fault experiments with multi-load loop were carried out. First, five-layer decomposition of main loop current was made by using wavelet packet. The effective coefficients were selected based on the change rate of a wavelet packet energy entropy before and after the arc fault occurs. Then, an effective signal of the arc fault was reconstructed. Second, the effective signal was decomposed into seven independent modes with a variational mode decomposition method. Its time–frequency distribution was obtained by solving the Wigner–Ville distribution and performing a linear summation of each mode. Third, the time-domain and time–frequency features of arc fault were extracted by analyzing the time-domain waveform and time–frequency distribution of the effective signal. An arc fault identification and line-selection model was established by using a support vector machine optimized by particle swarm optimization and grid search. Then, the accuracy of both arc fault detection and line selection were tested. Test results indicated that the proposed method can detect effectively arc fault and select fault line accurately.

SINGLE-PHASE GROUND FAULT LINE SELECTION METHOD IN ACTIVE DISTRIBUTION NETWORKS BASED ON HIGH-VOLTAGE INVERTER INJECTED SIGNALS

Fault line selection is a major problem of distribution networks. Selection of the fault line in distribution networks is increasingly becoming difficult due to the connecting of abundant new energy resources. A fault line selection method based on injected characteristic signals by the inverter was proposed in this study to increase accuracy of fault line selection. Under fault of distribution networks, the proposed method selected fault line by changing the modulation strategy of the inverter for a short time and injecting characteristic signals. A typical simulation model of active distribution networks was constructed to analyze different earth faults in distribution networks. Accuracy of this model was verified by simulation analysis. Results demonstrate that characteristic signals at different zero-sequence current measuring points are significantly different after signals are injected. Accordingly, the fault line during faults with 500 O or lower ground resistance is distinguished. Under the occurrence of faults with 500 O or higher ground resistance, the characteristic signal injection method based on an inverter has better energy value and can increase accuracy of line selection effectively compared with the traditional energy method. This study provides certain references for fault line selection in distribution networks based on inverters. Keywords: Distribution network, Single phase ground, Signal injection, Inverter, Fault line selection

A Fault Line Selection Method for DC Distribution Network Using Multiple Observers

This paper proposes a method of fault line selection for a DC distribution network. Firstly, the 1-mode current is calculated using the measured currents of the positive and the negative line. Then, it is time reversed and further decomposed by wavelet technology. Secondly, the lossless mirror line network is established according to the parameters and the topology of the DC distribution network. Thirdly, it is presumed that several virtual current sources are employed at the locations where the corresponding observers are, and the values of these current sources are equal to the processed 1-mode currents. Fourthly, a fault is placed at every point of the lossless mirror line network the RMS value of every assumed fault current is calculated. During this process, the phase coefficient of every lossless mirror line is set to vary along with the length of the line obeying Gaussian distribution. Finally, the line with the peak value of the RMS values of the currents is selected as the fault line. The result of fault line selection is updated using the fewest observers that are set in advance according to the initial result. A DC distribution network is simulated in PSCAD/EMDTC to verify the correctness of the proposed method.

Fault Line Selection Method for Resonant Grounding System Based on Improved PSO-SVM

The fault line selection of traditional resonant system method has low reliability and poor accuracy. Although the ordinary intelligent line selection method improves the line selection accuracy, the system has a long running time and is not effective. In this paper, an improved intelligent line selection method based on particle swarm optimization (PSO-SVM) is proposed. The traditional particle swarm optimization algorithm is improved, which makes the SVM model parameters faster and less likely to fall into local optimum. Combining the fifth harmonic method and the wavelet packet variation method to achieve efficient selection of grounding faults in resonant systems. The simulation results show that the proposed method has high accuracy and is not affected by factors such as grounding resistance and fault distance.

New method for transient line selection in distribution system based on grounding fault transferred

When single-phase arc grounding fault occurs in the neutral ungrounded distribution system, the fault can be transferred by artificially grounding the faulty phase of the busbar, which can effectively improve the fault handling capability of distribution system. The theoretical analysis shows that the characteristic of transient zero-mode current is obviously different between grounding fault and fault transferred. That is to say, in the two grounding processes, the directions of the transient zero-mode current of the fault line are opposite and the direction of the transient zero-mode current of the non-fault line is identical. So, the fault line can be determined based on this theory. The zero-mode current of each line is analysed by wavelet packet decomposition, and the feature band is selected according to the point of maximum energy. The correlation coefficient polarity between two reconstructed data, each is extracted from the first quarter of power cycle of grounding process in respective feature band, is used to determine the criterion for fault line selection. This method only uses its own line information to choose fault line without any other line information. Simultaneously, number of simulation results shows that the method is accurate and reliable.

A new approach to fault-line selection of small current neutral grounding system

ABSTRACTIn this paper, a new approach combining BP neural network with fuzzy Petri net (FPN) is developed to deal with the issue of fault-line selection of the small current neutral grounding system. First, the preliminaries of the FPN are briefly introduced. Then, the model of the fault-line selection is detailedly described to explain the new feature representation that fuses multiple fault features of the lines, including the wavelet energy, the active component and the fifth harmonic component. Finally, the simulation model of the fault-line selection is constructed, and the simulation experiments are carried out to verify the effectiveness of the new approach, which could be superior to the traditional fault-line selection approaches.

Fault-line selection and fault-type recognition in DC systems based on graph theory

When a fault occurs in a DC system, the fault current rises rapidly with no zero-crossing point which makes fault-line selection and fault-type identification difficult. In this paper, an online detection and protection method based on graph theory, namely the “double D method”, is proposed for fault-line selection and fault-type identification in DC systems. In the proposed method, the entire distribution network is visualized as a “map” with vertices representing the line convergence points and edges representing the connection lines. A network topology matrix “D” is formed by detecting the current directions as the current directions are altered following a fault, whereas the current directions at the ends of non-fault lines remain the same. In order to prevent misjudgment problems arising from power flow reversal, the rates of change of the fault currents are used to further determine whether a fault has occurred and the “double D method” is introduced to identify the fault type. Simulations results with different fault types verify the effectiveness and reliability of the proposed method.

Study on wide-area traveling wave fault line selection and fault location algorithm

Study on wide-area traveling wave fault line selection and fault location algorithm

Optimized Design and Research of Fault Line Selection Device for Indirect Grounding System Based on RTDS

Optimized Design and Research of Fault Line Selection Device for Indirect Grounding System Based on RTDS

Comprehensive Decision of Distribution Network Line Selection Based on Credibility Evaluation Index

小电流接地的配电系统故障选线技术是保证配电网安全可靠运行的重点。关于配电网选线技术研究主要集中于选线原理，且受配电系统网架结构、中性线接地方式、出线类型、负荷等因素影响，各方法均有局限性，目前缺乏对选线方法的综合评价的研究。本文针对三种不同选线方法，包括零序电流比幅法、谐波法和零序电流有功分量法，将每种选线方式的影响因素进行量化分析，定义了不同方法能够正确选线的可信度评价指标，并对其进行可信度等级划分，通过构造欧式距离量化指标评价体系，建立了综合评价指标，给出基于可信度评价的选线方式综合决策。最后，采用算例验证了该方法的可行性和实用性。 The fault line selection technology of distribution system with small current grounding is the key to ensure the safe and reliable operation of distribution network. The research on the technology of line selection in distribution network mainly focuses on the principle of line selection. Due to the influence of the grid structure of distribution system, the way of neutral grounding, the outlet type, load and other factors, each method has its limitations. Currently, there is a lack of the study on comprehensive evaluation methods of line selection. In this paper, three different line selection methods, including zero-sequence current amplitude method, harmonic method and zero-sequence current active component method, are used to quantitatively analyze the influencing factors of each line selection method. The credibility evaluation index of different methods is defined and classified according to the credibility level. The comprehensive evaluation index is established through the construction of Euclidean distance quantitative index evaluation system, and the comprehensive selection of line selection method based on credibility evaluation is given. Finally, an example is given to verify the feasibility and practicability of this method.

Research of Single-phase Grounding Fault Simulation for Small Current Grounding System

Our country generally uses small current grounding system (the neutral point no direct grounding system). Due to the fault current is too small to identify. So the distribution network operators have been plagued by this kind of fault line selection accuracy. This research establishes simulink model to analyze the fault of single-phase grounding system with matlab. The simulation and the results are used to guide the fault line selection.

A Method of Fault Line Selection in Small Current Grounding System Based on VMD Energy Entropy and Optimized K-means Clustering

When single-phase to ground fault occurs in small current grounding system, the zero-sequence transient current contains a lot of complex fault information. In order to extract characteristic from the zero-sequence current accurately as the criterion of fault line selection, this paper puts forward a method of fault line selection based on VMD energy entropy and optimized K-means clustering. Firstly, the zero-sequence current of each line is decomposed by VMD (variational mode decomposition, VMD), and each sub mode of the original current is obtained. Secondly, the energy entropy of each sub mode is calculated as the characteristic quantity of each line state. Finally, the optimized K-means clustering algorithm is used to cluster the fault feature to realize fault line selection. In this paper, Simulink is used to simulate the single-phase to ground fault in resonant grounded system and the results show that the method that has high accuracy can provide reference for practical engineering application.