Fault Diagnosis of Grid-Connected PV Inverters Based on Blind Source Separation and Machine Learning
Aiming at the problem that the open circuit fault of a single switching tube in grid-connected photovoltaic inverters is not easily detected,a diagnosis method based on blind source separation and machine learning was pro-posed.Firstly,FastICA algorithm is used to determine the open-circuit fault of a single switch tube.Secondly,the eigenvalues of rotating current in time domain and frequency domain are extracted.Finally,the machine learning model is trained with the characteristic value of rotating current as input and inverter operating state coding as out-put,and the model is cross-verified.Simulation results show that the open circuit fault diagnosis accuracy of this method can reach more than 98.9%,which has high application value.
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