基于小波变换与神经网络松散结合的低压电弧故障识别方法

Haoying Gu, Feng Zhang, Zijun Wang, Qing Ning, Shiwen Zhang
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引用次数: 12

摘要

据统计,低压电弧故障已成为导致电气火灾的主要因素之一。传统的电路保护装置不能有效地检测电弧故障。因此,对电弧故障检测技术的研究具有重要的现实意义和应用前景。提出了一种基于小波变换与神经网络松散结合的电弧故障识别方法。为了实现对不同载荷测试样本的识别,对采集到的电流波形进行小波分解,得到每一层的高频能量,然后将这些特性输入到BP神经网络中,构成一个松散的小波神经网络。采用自适应学习率和动量项来提高学习速度。比较了两种输入层节点选择方案,较优方案的准确率达到95%。采用平均冲击值法,验证了输入层提取特征的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification method for low-voltage Arc fault based on the loose combination of wavelet transformation and neural network
According to statistics, the low-voltage arc fault has become one of the primary factors leading to electrical fires. It's the fact that traditional circuit protecting devices cannot detect arc fault effectively. Therefore, there are great practical significance and prospect of application in research on arc fault detecting technology. In this paper, the identification method for arc fault based on the loose combination of wavelet transformation and neural network is proposed. In order to realize the recognition of the testing samples of diverse loads, the high-frequency energy in each layer is obtained through decomposing the acquired current waveforms by wavelet, and then these properties are inputted into back-propagation (BP) neural network to constitute a loose wavelet neural network. The adaptive learning rate and momentum term are used to improve the learning speed. Two selecting schemes of nodes in input layer are compared, and the accuracy rate of better scheme reaches 95 percent. By using mean impact value method, the validity of the extracted characteristics in input layer is verified.
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