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《Transactions of China Electrotechnical Society》 2015-02
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Fault Diagnosis in Solar Photovoltaic Grid-Connected Power System Based on Fault Tree and BAM Neural Network

Li Lianbing;Zhang Xiuyun;Wang Zhihua;Wang Zhiqiang;Hebei University of Technology;Tianjin University of Technology and Education;  
The fault diagnosis location and diagnosis causation of grid-connected solar photovoltaic systems are described, and the algorithm of fault tree(FT), the structure and algorithm of bidirectional associative memory(BAM) neural network are also described. Aimed at the faults of grid-connected solar photovoltaic system, the method of fault diagnosis based on fault tree and bidirectional associative memory neural network is brought out in this paper. The priori knowledge and experience of system diagnosis are introduced to fault tree analysis(FTA). The learning sample of BAM neural network is deduced by the corresponding relations between the fault modes and the fault analysis. The experiments and application of solar photovoltaic grid-connected fault diagnosis system show that this method has good real-time performance and effectiveness.
【Fund】: 国家自然科学基金青年基金(51107030);; 河北省自然科学基金青年基金(E2012202070)资助项目
【CateGory Index】: TM615
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