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

3rd International Conference on Energy Engineering and Environmental Protection

能源学;生态环境科学

Wang, Zhongfeng^1^2 ; Li, Ligang^1^2 ; Yang, Xiao^3 ; Guan, Ming^4 ; Li, Yanan^4 ; Zhou, Bowen^3

Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang

110016, China^1

Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang

110016, China^2

Northeastern University, Shenyang

110000, China^3

State Grid Liaoning Electric Power Co. Ltd., Anshan Electric Power Supply Company, Anshan

114000, China^4

关键词: BP neural networks;    Data analysis system;    Fault diagnosis model;    Operation and maintenance;    Photovoltaic modules;    Photovoltaic panels;    Photovoltaic systems;    Power generation efficiency;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/227/5/052063/pdf
DOI  :  10.1088/1755-1315/227/5/052063

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

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.