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.
关键词
光伏电站/故障诊断/随机欠采样/集成学习
Key words
photovoltaic power station/fault diagnosis/random under-sampling/ensemble learning
维普
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