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High-impedance fault detection in medium-voltage distribution network using computational intelligence-based classifiers

机译：使用基于计算智能的分类器中压配电网中的高阻抗故障检测

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摘要

This paper presents the high-impedance fault (HIF) detection and identification in medium-voltage distribution network of 13.8 kV using discrete wavelet transform (DWT) and intelligence classifiers such as adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM). The three-phase feeder network is modelled in MATLAB/Simulink to obtain the fault current signal of the feeder. The acquired fault current signal for various types of faults such as three-phase fault, line to line, line to ground, double line to ground and HIF is sampled using 1st, 2nd, 3rd, 4th and 5th level of detailed coefficients and approximated by DWT analysis to extract the feature, namely standard deviation (SD) values, considering the time-varying fault impedance. The SD values drawn by DWT technique have been used to train the computational intelligence-based classifiers such as fuzzy, Bayes, multi-layer perceptron neural network, ANFIS and SVM. The performance indices such as mean absolute error, root mean square error, kappa statistic, success rate and discrimination rate are compared for various classifiers presented. The results showed that the proffered ANFIS and SVM classifiers are more effective and their performance is substantially superior than other classifiers.
机译：本文介绍了使用离散小波变换（DWT）和智能分类器（如自适应神经模糊推理系统（ANFIS）和支持向量机（SVM）的离散小波变换（DWT）和智能分配器中的高阻抗断层网络检测和识别和识别。 ）。三相馈线网络在MATLAB / Simulink中建模以获得馈线的故障电流信号。所获取的故障电流信号，用于各种故障，如三相故障，线路，线到地，双线到地和HIF采样使用1st，2nd，3，第4和第5级详细系数和近似考虑到时变故障阻抗，DWT分析提取特征，即标准偏差（SD）值。 DWT技术绘制的SD值已被用于训练基于计算的基于计算智能的分类器，例如模糊，贝叶斯，多层Perceptron神经网络，ANFIS和SVM。比较诸如平均绝对误差，根均方误差，κ统计，成功率和歧视率的性能指标，以获取各种分类器。结果表明，提供的ANFI和SVM分类器更有效，其性能大大优于其他分类器。

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