Research on Photovoltaic Power Station Islanding Detection Based on Wavelet Transform and BP Neural Network

doi:10.3969/j.issn.1000-7229.2019.12.014

Abstract

Abstract: With the increasing scale of photovoltaic power station and the impact of grid-connected photovoltaic systems on the original power supply network， islanding detection has become a problem that PV power station must study in depth. Aiming at the deficiencies of the existing islanding detection methods， a novel passive islanding detection method based on wavelet transform and BP neural network is proposed. The method obtains the characteristic information of the signal before and after islanding through wavelet transform， and then the BP neural network implements islanding detection and islanding protection behavior according to this characteristic information. The simulation results show that the new passive islanding detection method described in this paper has high detection speed and small no-detection zone. In the case of multiple load quality factors and harmonics， no misjudgment of islanding detection will occur.

Key words: islanding detection, wavelet transform, neural network, eigenvector

CLC Number:

TM 76

JIANG Wenchao， ZHANG Xing， XIE Dong， LI Ming. Research on Photovoltaic Power Station Islanding Detection Based on Wavelet Transform and BP Neural Network[J]. Electric Power Construction, 2019, 40(12): 113-119.

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