Various faults of photovoltaic (PV) modules inevitably occur in the work process, since PV modules are installed in hostile situation. To obtain the types of failure, a novel fault diagnosis method based on back propagation (BP) neural network with Levenberg-Marquardt (L-M) algorithm for PV modules is proposed. Through the in-depth analysis the output of PV modules under normal and fault conditions, the input variables of the diagnosis model are acquired. The high-speed and real-time fault diagnosis model for PV modules is first designed based on TMS320VC5402 DSP and long-distance wireless fault diagnosis is realized by Zigbee technology. The simulation and experimental results show that the fault diagnosis method for PV modules based on BP network with L-M algorithm can effectively detect four types of fault for PV modules such as open circuit, short circuit, partial shading and abnormal degradation. The numerical results verify the effectiveness and correctness of the proposed method, which can provide a great educational benefit of PV operation technology.

光伏（PV）模块不可避免地会在工作过程中发生各种故障，因为光伏模块是在敌对状态下安装的。为了获得故障的类型，提出了一种基于反向传播（BP）神经网络和Levenberg-Marquardt（LM）算法的光伏组件故障诊断方法。通过深入分析光伏组件在正常和故障条件下的输出，获取诊断模型的输入变量。首先基于TMS320VC5402 DSP设计了光伏组件的高速实时故障诊断模型，并通过Zigbee技术实现了远程无线故障诊断。仿真与实验结果表明，基于LM网络的BP网络的光伏组件故障诊断方法可以有效地检测出光伏组件的四类故障，例如开路，短路，部分阴影和异常退化。数值结果验证了所提方法的有效性和正确性，可以为光伏发电技术提供很大的教育意义。