In response to the challenge of sample data imbalance in fault diagnosis methods for photovoltaic power plants based on machine learning,the paper proposes a fault diagnosis method leveraging an enhanced BP-Bagging algorithm.Firstly,a mapping relationship between photovoltaic data and fault types is established using a BP neural network to achieve fault diagnosis in photovoltaic systems.Subsequently,the Bagging algorithm is enhanced by uti-lizing random under-sampling(RUS)to address the issue of class imbalance in samples.Furthermore,to tackle the problem of overfitting in the BP network,the paper introduces a fault diagnosis model for photovoltaic power plants based on the enhanced BP-Bagging.This involves parallel training of multiple BP networks and determining fault di-agnosis results through a voting method.Finally,the paper conducts comparative experiments with different algo-rithms,calculates evaluation metrics related to model accuracy,and validates that the proposed method demon-strates high overall performance.To a certain extent,it effectively mitigates the challenge of sample class imbalance in fault diagnosis of photovoltaic power plants,thereby improving the accuracy of fault diagnosis in such systems.
photovoltaic power stationfault diagnosisrandom under-samplingensemble learning
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