Fault diagnosis method of microgrid system based on improved deep residual shrinkage network
Photovoltaic power generation may encounter such problems as randomness of output,being easily affected by meteorological and environmental factors,and aptness to be subjected to wiring mode and the internal health status of photovoltaic cell modules.Aiming at solving the above prob-lems,the waveform images of the photovoltaic array output voltage and the output current of each branch were taken as the input of the fault diagnosis model.Furthermore,CNN and DRN in typical deep learning algorithms were improved.DRSN was suitable for 2D image type identification and has better feature extraction performance,which was used as the PV array fault diagnosis algorithm.The simulation model of grid-connected photovoltaic power generation system was built in Matlab/Simu-link,and the corresponding test platform was built.The output voltage of photovoltaic array and out-put current of each branch under normal operation and various faults were measured respectively,and the corresponding waveform characteristic map was drawn as the input sample of DRSN algorithm.Thus the fault classification and identification of grid-connected photovoltaic array are realized.Nu-merical simulation and experiments verify the correctness and superiority of DRSN model.The results of comparative analysis show that the accuracy of DRSN algorithm in grid-connected photovoltaic ar-ray fault diagnosis simulation is significantly higher than that of CNN and ResNet algorithm,and thus the algorithm has better training effect and classification performance.
deep residual shrinkage networkfault diagnosisgrid-connected photovoltaic power generation systemwaveform characteristic map