基于功率信号分析的光伏电站故障诊断方法OA北大核心CSTPCD

To improve the fault diagnosis accuracy of a PV power plant fault,a fault diagnosis method for PV power plant based on power signal analysis is proposed.First,a convolutional neural networks-long short-term memory(CNN-LSTM)network model and a ridge regression model are used to mine the time series information about the historical power generation data,and the dynamic time warping(DTW)distance between the actual and predicted power genera-tion is selected to detect fault.Second,a fault classification index based on the frequency-domain characteristics of ac-tual power generation is put forward,and the classification rules are built to classify the power plant faults into commu-nication fault,equipment fault and power cut fault,and the fault impact equivalent power generation hours are com-bined to assess the degree of each type of fault.Finally,the analysis of an example verifies the effectiveness of the pro-posed algorithm.