Islanding detection based on wavelet transform and neural network

被引：0

作者：

Xie, Dong ^{[1
]}

Zhang, Xing ^{[1
]}

Cao, Renxian ^{[2
]}

机构：

[1] School of Electric and Automatic Engineering, Hefei University of Technology, Hefei 230009, Anhui Province

[2] Sungrow Power Supply Co. Ltd, Hefei 230009, Anhui Province

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2014年 / 34卷 / 04期

关键词：

Characteristic variable; Distributed generation; Islanding detection; Neural network; Wavelet transform;

D O I：

10.13334/j.0258-8013.pcsee.2014.04.004

中图分类号：

学科分类号：

摘要：

The function of islanding detection is required for the grid-connected inverter-based distributed generation system due to safety reasons and to maintain the quality of power supply. Passive methods have a large non detection zone and the detecting time is long, while active schemes have negative influence on power quality, so a novel passive islanding detection method was proposed. In this method, wavelet transform was adopted to extract feature vectors from the voltage of point of common coupling (PCC) point and the output current of inverter, and then pattern recognition was exerted by BP neural network to determine whether there was an island phenomenon. The simulation and experiment results show that this method is faster than the traditional passive methods in islanding detection, and the non-detection zone is smaller. At the same time, because no disturbance was added to the control signal in the method, there isn't a negative impact on power quality. The method overcomes the shortcoming of active methods and has high accuracy and reliability. © 2014 Chin. Soc. for Elec. Eng.

引用

页码：537 / 544

页数：7

共 15 条

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