Fault diagnosis and operation and maintenance of PV components based on BP neural network with data cloud acquisition

In order to improve the power generation efficiency of photovoltaic systems and to reduce the cost of manual maintenance, this paper proposes a fault diagnosis and operation and maintenance method based on BP neural network with data cloud acquisition. The causes of short circuit and abnormal aging failure of photovoltaic modules are analyzed. BP neural network fault diagnosis model is established in Matlab, combined with the mathematical model of photovoltaic components to simulate the output of various faults, performing sample training on the fault diagnosis model. The data collected by the distributed photovoltaic information processing and data analysis system platform is used as the input data of the neural network. According to the diagnosis result, reasonable suggestions for PV module operation and maintenance are provided from the aspects of component maintenance, photovoltaic panel cleaning, dip angle change, etc. Simulation results verify the correctness and effectiveness of the proposed method.