基于质心频率和BP神经网络的配网故障测距

doi:10.19595/j.cnki.1000-6753.tces.171242

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Abstract Low frequency component contents of zero-sequence currents increase with the growth of fault distance, which makes current spectrums appear left-shift tendency. In addition, the closer fault closing angle approaches to 90°, the more high frequency component contents of zero-sequence currents are, which opposites to the case that transient resistance increase. Hereby, this paper proposes a frequency domain fault location method based on centroid frequency. Centroid frequency can be used to describle current spectrums entire distribution feature, further, the influence on current spectrums by transient resistance, fault distance and fault closing angle can be describled by centroid frequency. By utilizing BPNN (back propagation neural networks), the mapping relationship can be simulated between transient resistance, fault distance, fault closing angle and centroid frequency, which realizes fault loaction. In the case that centroid frequency fault distance curves are not monotonous, an assitant criteria is given, further, final resulsts are obtained by centroid frequency fault distance curves combined with the assitant criteria. Results prove that the method proposed by this paper has a distinct advantage than other frequency domain methods in the terms of fault location in hybrid line or feeder line with branches and apply to complex structures in distribution networks. Moreover, accurate fault location results can be obtained when training samples lack in quantity and accuracy.

Key words： Neutral grounded via arc extinguishing coil frequency domain fault location methods centroid frequency BP neural networks

Received: 29 August 2017 Published: 14 September 2018

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TM77

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	Articles by authors
	Zhou Niancheng
	Xiao Shuyan
	Yu Yinshu
	Wang Qianggang

Cite this article:

Zhou Niancheng,Xiao Shuyan,Yu Yinshu等. Fault Location in Distribution Networks Based on Centroid Frequency and BP Neural Networks[J]. Transactions of China Electrotechnical Society, 2018, 33(17): 4154-4166.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.171242 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I17/4154

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