Fault Location In Distribution Networks Research Articles

Fault Location and Phase Selection Method of Distribution Network Containing IIDG Based on Online Active Detection

This paper proposes a fault location and phase selection method for the distribution network containing IIDG based on online active detection, utilizing online active detection. In this method, the characteristic harmonic current injection control strategy based on d- and q-axis control is obtained by analyzing the structure, control strategy, and short-circuit current of IIDG. During a fault, the distributed power supply injects characteristic harmonic current into line, adapting its magnitude with the short-circuit current’s size. By detecting the difference in harmonic current content rate for both ends of the fault point, the method achieves online location and line selection of fault points. A 10 kV IIDG distribution network model is simulated by using PSCAD to validate the feasibility and accuracy of the proposed fault location and phase selection method.

Flexible DC distribution network fault location method based on beetle swarm optimization algorithm

To improve the fault location ability of the DC distribution network and ensure its power supply reliability, a fault location method based on the beetle swarm optimization algorithm is proposed. Firstly, the fault characteristics of the DC side after short-circuit fault are analyzed, and the mathematical model for fault location is established; then, the problem of fault location is transformed into the problem of parameter identification by discretization; finally, to verify the effectiveness of the method, a flexible DC distribution network model is built in MATLAB. The simulation results show that the proposed fault location method is accurate, not affected by the transition resistance, and has good robustness.

Retracted: Realization of Single-Phase Grounding Fault Location and Recovery Technology in Distribution Network

Retracted: Realization of Single-Phase Grounding Fault Location and Recovery Technology in Distribution Network

A novel superimposed voltage energy‐based approach for single phase to ground fault detection and location in distribution networks

AbstractThe structure complexity and the extended geographical area are among the factors that create challenges in accurate fault location and detection in distribution networks. The single‐phase to ground (SPG) faults are considered as most frequent reasons for distribution network interruption, which can threaten the network's reliability. Signal processing methods are usually used as common approaches to distinguish and locate faults in distribution networks. This paper proposes a novel scenario to select optimal wavelet packet transform (WPT) coefficients for SPG fault location and a method based on superimposed voltage energy to distinguish the faulty phase according to these coefficients. Furthermore, employing the energies of the superimposed voltages helps to minimize the effects of high resistance faults (HRFs) and load encroachment on the efficiency of the faulty phase detection part. Comparing the obtained results with other scenarios demonstrates the considerable efficiency of the proposed scenario. Finally, with the help of general regression neural networks (GRNN) as machine‐learning tools, a new algorithm is derived for detecting and locating the SPG fault and capacitor bank switching overvoltage (COV). Simulation results are implemented on the IEEE 34‐bus standard distribution network, demonstrating the efficiency and superiority of the suggested method.

A New Method for Fault Detection and Location in a Low-Resistance Grounded Power Distribution Network Using Voltage Phasor of D-PMUs Data

A New Method for Fault Detection and Location in a Low-Resistance Grounded Power Distribution Network Using Voltage Phasor of D-PMUs Data

Two-stage fault section location for distribution networks based on compressed sensing with estimated voltage measurements

Methods for fault location in distribution networks based on compressed sensing (CS) have been investigated and the results have demonstrated some degree of feasibility over the past years. However, in the case of a fault occurring close to a line’s terminal, the existing methods typically cannot locate the fault section correctly but just locate the fault to the bus near the fault point or adjacent section rather than the fault section. To improve the accuracy of fault location, a two-stage fault section location method is proposed in this paper. In the first stage, the CS-based model is solved by using all the measured voltages, and the fault candidate sections are identified. In the second stage, the node impedance matrix of the subnetwork formed by the fault candidate sections are calculated, and the voltage amplitudes of the buses around this subnetwork are estimated, which are then used to solve the CS-based model to finally determine fault section. Validations are implemented on a modified IEEE 33 bus system and a modified 134-bus system. The results verify a satisfactory performance in fault section location under various fault conditions.

Fault Location of Distribution Network Based on Back Propagation Neural Network Optimization Algorithm

Research on fault diagnosis and positioning of the distribution network (DN) has always been an important research direction related to power supply safety performance. The back propagation neural network (BPNN) is a commonly used intelligent algorithm for fault location research in the DN. To improve the accuracy of dual fault diagnosis in the DN, this study optimizes BPNN by combining the genetic algorithm (GA) and cloud theory. The two types of BPNN before and after optimization are used for single fault and dual fault diagnosis of the DN, respectively. The experimental results show that the optimized BPNN has certain effectiveness and stability. The optimized BPNN requires 25.65 ms of runtime and 365 simulation steps. And in diagnosis and positioning of dual faults, the optimized BPNN exhibits a higher fault diagnosis rate, with an accuracy of 89%. In comparison to ROC curves, the optimized BPNN has a larger area under the curve and its curve is smoother. The results confirm that the optimized BPNN has high efficiency and accuracy.

Analysis of distribution network planning with distributed power supply based on ELM algorithm

Abstract With the development of the social economy, the importance of energy and environmental issues is becoming more and more prominent. Distributed power sources (DG) have the advantages of low carbon and environmental protection, flexible control, and low investment cost, but their nodes in the grid are random, which will increase the complexity and uncertainty of grid operation after grid connection. In this paper, we study the planning path of DGs in distribution networks and the protection technologies involved. Firstly, we focus on the impact of DG access on the protection of distribution networks and the impact of different access capacities and access locations of DG on the protection of distribution networks. Second, after studying the current status of the application of traditional genetic algorithms in distribution network fault location, a two-layer planning model based on the modified extreme learning machine algorithm (MPSO-ELM) is proposed, with the load demand guaranteed by the first layer of power allocation and the fault location incorporated into the distribution network protection scheme in the second layer. Finally, simulations are performed to compare and demonstrate the advantages of this MPSO-ELM algorithm. The simulation results show that the MPSOELM algorithm converges faster than the traditional genetic algorithm by 7s, the average operating speed of power nodes is 3.61ms faster than the traditional genetic algorithm by 5.59ms, and the accuracy in fault location is improved by 23.14%. The simulation results verify the comprehensiveness, accuracy, and high efficiency of the ELM algorithm for double-layer planning of distribution networks containing distributed power supplies, which is important for improving the level of power grid construction and avoiding the risk of power grid construction.

Modal Transformation based Fault Location in Radial Distribution Network

This paper introduces the technique of fault distance estimation based on modal transformation and signal processing. The recorded faulted phase currents are applied to the Karrenbauer model transformation and these model component currents are decomposed into detail coefficients by the use of Daubechies wavelet, db6. The fault recorder installed at the terminal of the feeder records different time delays between the modal components. In order to find fault distance, the time delay values and modal components velocity are used in traveling wave theory. This paper compares two different conditions: the first condition does not use a modal transformation and the second condition uses a modal transformation. When using modal transformation conditions, three different coefficient levels (detail coefficient level 1 (D1); the combination of detail coefficient level 1+2 (D1+2) and the combination of detail coefficient level 1+2+3 (D1+2+3) ) are used to estimate the fault distance. Different fault types with different fault locations are created in MATLAB simulation.

Grounding fault location method of overhead line based on dual-axis magnetic field trajectory

To address the challenges in fault location in distribution networks, the distribution of magnetic field under overhead line and its relationship with three-phase currents are explored in this paper. At the same time, considering the influence of sensor installation position, line sag and galloping on magnetic field, a grounding fault location method of an overhead line based on dual-axis magnetic field trajectory is proposed. The analytical expressions of the magnetic field on the x-axis and y-axis under the overhead line are obtained by least squares fitting. The Lissajous figure synthesized by dual-axis is then compared with the general equation of an ellipse, and the characteristic quantity expression characterizing the magnetic field trajectory structure is obtained. Finally, a fault location criterion is constructed using the difference of the characteristic quantities of the ellipses synthesized by x-axis and y-axis magnetic fields upstream and downstream of the fault point, i.e., the difference of the length of the major axis and the minor axis, and the sign for the ratio of the cosine value of the inclination angle. Compared with other location methods based on electrical quantity, the principle of this method is simpler and it can locate faults more quickly and accurately. A large number of simulation results show that the proposed method is suitable for different types of fault conditions.

Fault Location in Distribution Network by Solving the Optimization Problem Based on Power System Status Estimation Using the PMU

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

Research on Fault Location in DC Distribution Network Based on Adaptive Artificial Bee Colony Slime Mould Algorithm

Research on Fault Location in DC Distribution Network Based on Adaptive Artificial Bee Colony Slime Mould Algorithm

Distribution network fault diagnosis method for the deep integration of cyber-physics

• A distribution network fault location method for deep integration of cyber-physics is proposed. • The complexity of fault diagnosis is reduced by building a library of faulty suspect components. • The characteristic data collected by the D-PMUs is used and improves the speed of fault diagnosis. • Petri net model is used to accurately describe the process of fault occurrence and improves the accuracy. The deep integration of cyber-physics provides multi-source information data for the fault analysis of distribution networks, but with conventional methods, it is difficult to select and use these data reasonably. This paper proposes a fault diagnosis method for a distribution network based on a distributed phasor measurement unit (D-PMU) and a Petri net. The method introduces a fault suspect component library formed with coupling pre-judgment and a fault diagnosis model, which simplifies the process of network analysis and improves the speed of the fault diagnosis. The method makes full use of the time constraint relationship of the alarm information, correctly selects the action time point, and achieves an improvement of the fault diagnosis accuracy. In addition, considering the problem that the measurement equipment coverage is not comprehensive in the actual distribution network, the proposed method inputs the characteristic information collected by the D-PMU at the typical measurement point into the Petri net model, analyzes the fault of the distribution network, and avoids the problem of the scheduling being risky due to the result of the probabilistic form. Finally, this paper describes the setup of some experimental analyses to verify the effectiveness of the proposed method.

SR-GNN Based Fault Classification and Location in Power Distribution Network

Accurately evaluating the fault type and location is important for ensuring the reliability of the power distribution network. A mushrooming number of distributed generations (DGs) connected to the distribution system brings challenges to traditional fault classification and location methods. Novel AI-based methods are mostly based on wide area measurement with the assistance of intelligent devices, whose economic cost is somewhat high. This paper develops a super-resolution (SR) and graph neural network (GNN) based method for fault classification and location in the power distribution network. It can accurately evaluate the fault type and location only by obtaining the measurements of some key buses in the distribution network, which reduces the construction cost of the distribution system. The IEEE 37 Bus system is used for testing the proposed method and verifying its effectiveness. In addition, further experiments show that the proposed method has a certain anti-noise capability and is robust to fault resistance change, distribution network reconfiguration, and distributed power access.

Fault Location Method of Distribution Network with Distributed Generation

Distributed generation is widely used in power systems, but the topology becomes complicated after the grid connection. Because of the large-scale distributed generation grid, the difficulty of fault location is increasing, and the terminal is easily affected by environmental influences, which leads to the uploaded information being easily distorted. This paper proposes an improved algorithm to solve the defects of the standard intelligent algorithm in the location of high-dimensional and large-scale distributed grids. At the same time, a hybrid algorithm combined with the improved matrix algorithm is proposed to solve the location of the fault section. The experimental analysis proves that the location method studied in this paper can guarantee the distribution network’s security and stability and the power supply’s reliability. The experimental analysis has significant engineering value in the distribution network fault location.

Anomaly Detection of Distribution Network Based on Adversarial Dual Autoencoder

Anomaly detection on attributed graphs aims to identify individuals or groups whose patterns deviate significantly from most samples, which is very important for internet security, power system, and other applications. Especially in the power system, the energy flow pattern of the distribution network is very similar to the graph topological constraints. Taking each equipment and bus as nodes and the relevant electrical measurement data as attributes, the distribution network fault location problem is mapped to the anomaly detection task on the attributed graph. The traditional GNN-based anomaly detection algorithm for distribution networks has some problems, such as over-smoothing and inefficient embedding learning. To solve the problem of anomaly detection in the distribution network, an anomaly detection algorithm based on adversarial dual autoencoder (DGAE_GAN) is proposed, which is composed of a structural encoder and attribute encoder based on residual connection GAT. Through joint learning, the attributed graph is mapped to the low-dimensional space, and the decoders reconstruct the attributed graph. Then, a discriminator is invited to learn the sample similarity, and the game strategy can effectively alleviate the over-smoothing problem. Finally, the anomaly node detection task is realized by calculating the reconstruction errors from both the structure and attribute levels. We conducted multiple experiments on three real-world datasets, and the IEEE standard example shows that our proposed model is superior to the existing methods.

Intelligent Wireless Monitoring Technology for 10kV Overhead lines in Smart Grid Networks

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.

Fault Location of Distribution Network Based on Multi-population Particle Swarm Optimization Algorithm

Fault location of the distribution network is an important direction in the construction of distribution automation. For the problem of slow convergence of intelligent optimization algorithms and easy to fall into local optimality, the multi-population particle swarm optimization algorithm is proposed. The algorithm is compared with single population particle swarm algorithm on IEEE69 node model, it is proved that the new algorithm can find fault location faster. Then the effectiveness of the algorithm in a variety of distribution network fault location scenarios is verified, including single fault location and multiple fault location, and the algorithm can also accurately locate when the input information is distorted which shows good fault tolerance.

A Comprehensive Method for Fault Location of Active Distribution Network Based on Improved Matrix Algorithm and Optimization Algorithm

The traditional fault location methods are not effective for the multisource active distribution network and are difficult to meet both requirements of timeliness and fault tolerance simultaneously. In this paper, a fault location method for active distribution networks which combines both improved matrix algorithm and optimization algorithm is proposed. The improved matrix algorithm is constructed to determine the fault section hypothesis by using the causal relationship between the alarm information and the fault section. Subsequently, network splitting is used for causal verification to check whether the alarm information is distorted. If the alarm information is normal, the fault sections can be quickly located by the above matrix algorithm. If the alarm information is distorted, the optimization model is constructed by using the fault section hypothesis selected by the matrix algorithm. The discrete particle swarm optimization (DPSO) algorithm is used to solve this optimization model, which can accurately locate the real fault sections. Hence, the advantages of both matrix algorithm and optimization algorithm are complementary. Simulation results show that the proposed method has the advantages of both high timeliness and fault tolerance in a complex active distribution network.

Realization of Single-Phase Grounding Fault Location and Recovery Technology in Distribution Network

In order to locate the single-phase-to-ground fault in distribution network, a research method of single-phase-to-ground fault location and recovery technology in distribution network is proposed. According to the characteristics of single-phase-to-ground fault in distribution network, the fault characteristics of zero-sequence current and three-phase current transient records are extracted by generalized S transform of correlation coefficient, and multi-source redundant information is formed. Fuzzy C-means clustering analysis method is used to fuse multiple fault features online location strategy. The simulation results show that the fuzzy clustering based on FCM changes continuously with the accumulation of samples, and the sample center changes with the enrichment of the samples. When the arc suppression coil is grounded with different initial phase angle transition resistance and intermittent fault, the state of each measuring point can be correctly identified, and the accurate energization rate is 97%. The test results show that the system can achieve accurate positioning for all sections.