Fault Detection in Microgrids Using Combined Classification Algorithms and Feature Selection Methods

S. Ranjbar, S. Jamali
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引用次数: 6

Abstract

Due to fault current variations over a wide range, protection strategies relying on high fault currents in microgrids are a big challenge. This paper proposes a method for fault detection in microgrids using data mining patterns and classification algorithms. For this reason, several short circuit fault and no-fault cases (i.e. load switching, motor starting and transformer energization) are generated and one cycle of the voltage and current signals is preprocessed by wavelet packet transform (WPT). The main features of voltage and current signals are extracted using detailed coefficients of the WPT. For discriminating faults from no-fault events, two different classifiers (i.e. random forest (RF) and K-nearest neighbors (K-NN)) are utilized. To improve the classifiers accuracy or reduce data storage requirements of relays, two filter based feature selection methods are applied on feature vector for choosing the most relevant features. For evaluating the performance of the complete and reduced feature vectors, the standard IEC microgrid is simulated for both islanded and grid connected modes of operation with meshed and radial structures. Test results show the effectiveness of the proposed method for fault detection.
基于分类算法和特征选择方法的微电网故障检测
由于微电网的故障电流变化范围很大,依赖于高故障电流的保护策略是一个很大的挑战。本文提出了一种基于数据挖掘模式和分类算法的微电网故障检测方法。为此,产生若干短路故障和无故障情况(即负载切换、电动机启动和变压器通电),并对一个周期的电压电流信号进行小波包变换预处理。利用WPT的详细系数提取电压和电流信号的主要特征。为了区分故障和非故障事件,使用了两种不同的分类器(即随机森林(RF)和k近邻(K-NN))。为了提高分类器的准确率或降低继电器的数据存储要求,在特征向量上采用了两种基于滤波器的特征选择方法来选择最相关的特征。为了评估完整和简化的特征向量的性能,对具有网格和径向结构的孤岛和网格连接模式的标准IEC微电网进行了模拟。实验结果表明了该方法对故障检测的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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