Fault Location In Distribution Networks Research Articles

Active Distribution Network Fault Location Based on Petri Nets and Improved Particle Swarm Optimization Algorithm

Active Distribution Network Fault Location Based on Petri Nets and Improved Particle Swarm Optimization Algorithm

Fault Location Method of Distribution Network Based on VGAE-GraphSAGE

The distribution network is the main component of the power system, which undertakes the important function of power transmission and distribution. Fast and accurate distribution network fault location is one of the important means to ensure the safe and stable operation of the power system and is helpful in guiding troubleshooting, shortening the power outage time, reducing the workload of manual inspection, and reducing the social and economic losses caused by faults. Due to the development of the new distribution network, the comprehensive influence of many factors has put forward new challenges to the traditional distribution network fault location methods. In this paper, a distribution network fault location method based on the graph neural network is proposed. Firstly, the distribution network is treated as non-Euclidean graph data; secondly, variational graph auto-encoders (VGAE) are used to mine the underlying information of nodes and improve the overall anti-noise performance of the fault location method. Then the GraphSAGE model is used to aggregate the neighbor information of nodes, fully consider the influence of the surrounding lines on the target lines, and improve the output of the model to locate the distribution network line where the fault occurred. The experimental example analysis based on OpenDSS simulation software (version 9.8) proves that the proposed method has high accuracy and anti-interference, and the accuracy reached 97.81%. Moreover, the positioning result is still good in the new intelligent distribution network scenario with distributed power access, with an accuracy of 95.07% in the hybrid power generation scenario.

Fault location method for new distribution networks based on waveform matching of time–frequency travelling waves

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

Intelligent distribution network fault monitoring integrating differential evolution and chaos whale optimization algorithm

Faced with rapid development and increasingly complex power grid structure structure of the power grid, it is necessary to accurately locate faults in modern intelligent distribution networks in order to ensure power supply reliability and improve power supply service quality. Aiming at the low positioning accuracy, long time consumption, and limited coverage types in existing positioning algorithms, a new intelligent distribution network fault positioning algorithm is designed by combining two algorithms to complete the monitoring task. Firstly, intelligent distribution network fault location methods under different distributed power grid connection methods are analyzed. Then, considering the distributed power grid connection, a fault location algorithm is designed by combining differential evolution algorithm, Sine chaotic mapping, and whale algorithm. The research results indicated that the designed method had good benchmark performance, with accuracy and recall values as high as 0.98 and 0.97, respectively. During the training process, it only required 175 iterations to reach a stable state. In practical applications, the accuracy of this algorithm in testing three types of faulty power grids was 98.90%, 98.41%, and 98.25%, respectively. The method can effectively improve the fault location accuracy, providing better positioning technology for fault monitoring problems in power systems.

Combination of Matrix and Genetic Algorithms for Fault Location of Active Distribution Network

Abstract Timely and effective location of faults in distribution networks is important for restoring power supply and dealing with faults. In this paper, a method combining matrix algorithm and genetic algorithm (GA) is proposed to determine the fault section. Firstly, the matrix algorithm is applied to locate the fault section initially by combining the alarm information uploaded from the feeder terminal unit (FTU). Secondly, in order to deal with the problem of outputting wrong location results, the switching function of the active distribution network is constructed to check the location results calculated by the matrix, and the results that do not meet the checking will be treated as a suspicious fault set, and the relevant evaluation function is constructed and optimized using the genetic algorithm, so as to arrive at the correct fault zone location results. The results of the example simulation show that the method used in this paper can achieve fast and fault-tolerant fault location in active distribution networks.

Deep Learning-based Intelligent Fault Diagnosis for Power Distribution Networks

Power distribution networks with distributed generation (DG) face challenges in fault diagnosis due to the high uncertainty, randomness, and complexity introduced by DG integration. This study proposes a two-stage approach for fault location and identification in distribution networks with DG. First, an improved bald eagle search algorithm combined with the Dijkstra algorithm (D-IBES) is developed for fault location. Second, a fusion deep residual shrinkage network (FDRSN) is integrated with IBES and support vector machine (SVM) to form the FDRSN-IBS-SVM model for fault identification. Experimental results showed that the D-IBES algorithm achieved a CPU loss rate of 0.54% and an average time consumption of 1.70 seconds in complex scenarios, outperforming the original IBES algorithm. The FDRSN-IBS-SVM model attained high fault identification accuracy (99.05% and 98.54%) under different DG output power levels and maintained robustness (97.89% accuracy and 97.54% recall) under 5% Gaussian white noise. The proposed approach demonstrates superior performance compared to existing methods and provides a promising solution for intelligent fault diagnosis in modern distribution networks.

Fault Location in Distribution Network Based on RNN and Transfer Learning

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%.

A Novel DWT-ERT-based Fault Location for Distribution Network

Background: A new DWT-ERT-based fault location method is suggested in the IEEE test feeder. Objective: The fault location approach in the distribution network has been proposed in this pa-per that utilizes the discrete wavelet transform (DWT) and ensemble regression tree (ERT). Methods: The fault location methodology has been validated by simulations conducted on an IEEE 13 bus node test feeder. Results: The results show that the suggested solution has low compute burden and memory re-quirements, and is unaffected by system and fault situations. Conclusion: In this study, the fault location approach for the distribution system employing DWT and ERT has been proposed.

Low-voltage characteristic voltage based fault distance estimation method of distribution network

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.

Earth Fault Location for Non-Directly Grounded Distribution Networks

Earth Fault Location for Non-Directly Grounded Distribution Networks

Active Distribution Network Fault Diagnosis Based on Improved Northern Goshawk Search Algorithm

Timely and accurate fault location in active distribution networks is of vital importance to ensure the reliability of power grid operation. However, existing intelligent algorithms applied in fault location of active distribution networks possess slow convergence speed and low accuracy, hindering the construction of new power systems. In this paper, a new regional fault localization method based on an improved northern goshawk search algorithm is proposed. The population quality of the samples was improved by using the chaotic initialization strategy. Meanwhile, the positive cosine strategy and adaptive Gaussian–Cauchy hybrid variational perturbation strategy were introduced to the northern goshawk search algorithm, which adopted the perturbation operation to interfere with the individuals to increase the diversity of the population, contributing to jumping out of the local optimum to strengthen the ability of local escape. Finally, simulation verification was carried out in a multi-branch distribution network containing distributed power sources. Compared with the traditional regional localization models, the new method proposed possesses faster convergence speed and higher location accuracy under different fault locations and different distortion points.

A novel fault location method for the active distribution network based on dynamic quantum genetic algorithm

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.

Fault location of distribution network with distributed generation based on Karrenbauer transform and support vector machine regression

Fault location of distribution network with distributed generation based on Karrenbauer transform and support vector machine regression

A new method for estimating fault location in radial medium voltage distribution network only using measurements at feeder beginning

A new method for estimating fault location in radial medium voltage distribution network only using measurements at feeder beginning

Cable Fault Location in Distribution Network Based on Decomposition of Time Reversal Operator

Cable Fault Location in Distribution Network Based on Decomposition of Time Reversal Operator

Regional Fault Location of Distribution Network based on Distributed Observation and Fusion of Multi-Source Evidence

Regional Fault Location of Distribution Network based on Distributed Observation and Fusion of Multi-Source Evidence

Line fault location of flexible DC distribution network based on adaptive noise empirical mode decomposition

A fault location method for flexible DC distribution network lines is proposed to address the issues of insufficient decomposition in fault signal extraction and the complexity of traveling wave head detection. The method combines adaptive noise empirical mode decomposition (CEEMDAN) and Teager energy operator (TEO). Firstly, CEEMDAN is used to adaptively denoise the line mode signal and obtain the optimal modal component to overcome modal aliasing and insufficient decomposition in signal separation. Then, the reach time of the fault traveling wave is determined by using TEO. Ultimately, the fault location is accurately determined by employing both ends of the traveling wave ranging technique. Through the combination of software PSCAD and MATLAB, the simulation experiment of the line fault is carried out. This means that the method can quickly and precisely identify the location of faults in a system.

Fault Location in Active Distribution Network Under Hybrid Classical Quantum Computing Architecture

Fault Location in Active Distribution Network Under Hybrid Classical Quantum Computing Architecture

Application ofQGAalgorithm improved by gradient descent in fault diagnosis and location of distributed distribution network

AbstractA fault diagnosis and localization approach for distributed distribution networks is created using an upgraded quantum genetic algorithm to swiftly identify and detect flaws in the network. In this method, the dynamic rotation strategy in gradient descent method is used to update the quantum gate to enhance the convergence speed, that is, the gradient descent quantum genetic algorithm is constructed. The results of single fault and multiple fault simulation test on the distribution network model of regional node of distributed power supply show that the average iteration of gradient descent quantum genetic algorithm 85.36, 86.35, 88.24, and 88.69 times can reach the target optimal value. In four different cases, the algorithm of gradient descent quantum genetic algorithm can reach the optimal by iterating 88, 91, 92, and 90 times, respectively. Compared with other algorithms, the convergence rate of gradient descent quantum genetic algorithm is the fastest in the four experimental cases. The consistency between the output score and the real score of the gradient descent quantum genetic algorithm is above 0.9. The results above show that the algorithm is effective. The optimization ability and stability of the algorithm are also stronger, and it has certain application potential.

A New Fault Distance Measurement Scheme for DC Distribution Network

To solve the problem of fault location in the DC distribution network, this paper proposes a new fault distance measurement scheme based on the impedance measurement algorithm against fault transition resistance and the improved Moore voting algorithm. Firstly, signals of different frequencies are injected into the DC circuit in different periods, after the converter is blocked due to the DC side fault. Then, through the distance measurement algorithm against fault transition resistance, several groups of fault distance data are obtained. The improved Moore voting algorithm is used to process these fault distance values so that the accurate fault distance values are determined. Finally, simulation tests verify the reliability and accuracy of the proposed scheme. The proposed scheme requires a low sampling frequency, and it can not be interfered with by the fault transition resistance and occasional large errors. These advantages help to improve the efficiency and accuracy of troubleshooting.