Abstract：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.
作者简介: 李练兵 男,1972年生,教授,硕士生导师,研究方向为电力电子与新能源技术。张秀云 女,1988年生,硕士研究生,研究方向为新能源转换理论与控制工程。 故障树分析法（Fault Tree Analysis,FTA）是美国贝尔电话研究所的沃森（Watson）和默恩斯（Mearns）于1961~1962年在民兵式导弹发射控制系统的设计中开发的,是一种将系统故障形成原因按树枝状逐级细化的图形演绎方法,把所研究系统的最不希望发生的故障状态作为故障分析的目标,然后逐级寻找导致这一故障发生的因素,直至无需再探究的因素为止,是国际上公认的一种直观、有效的可靠性分析和故障诊断方法。
引用本文:
李练兵,张秀云,王志华,王志强. 故障树和BAM神经网络在光伏并网故障诊断中的应用[J]. 电工技术学报, 2015, 30(2): 248-254.
Li Lianbing,Zhang Xiuyun,Wang Zhihua,Wang Zhiqiang. Fault Diagnosis in Solar Photovoltaic Grid-Connected Power System Based on Fault Tree and BAM Neural Network. Transactions of China Electrotechnical Society, 2015, 30(2): 248-254.
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2015, Vol. 30 
