Abstract
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.
Highlights
Distribution networks in China are characterized by complex structures, large scale, wide coverage, and frequent ground faults, and more than 80% of these faults are single-phase ground faults [1]
The main methods used for identifying singlephase ground fault location in distribution networks are the impedance [4], [5], S-injection [6], [7], traveling wave [8] and port fault diagnosis methods [9]
Reference [11] presents a unified matrix algorithm for fault section judgment and isolation of distribution automation system based on remote terminal unit (RTU)
Summary
Distribution networks in China are characterized by complex structures, large scale, wide coverage, and frequent ground faults, and more than 80% of these faults are single-phase ground faults [1]. A fault location method for distribution network based on advanced genetic algorithm is proposed in [10] This method has high fault tolerance and can be used in complex situations of multiple sources and multiple faults. Reference [11] presents a unified matrix algorithm for fault section judgment and isolation of distribution automation system based on remote terminal unit (RTU) This method requires significant computation, and terminal fault judgment is limited to single power supply systems. Reference [12] proposes a phasor measurement unit (PMU)-based fault location method for multi-terminal transmission lines.
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