四川轻化工大学学报（自然科学版）2025，Vol.38Issue(1)：57-68,12.DOI:10.11863/j.suse.2025.01.07
基于ISSA-BP神经网络的光伏阵列故障诊断方法
Fault Diagnosis Method for Photovoltaic Array Based on ISSA-BP Neural Network
摘要
Abstract
In response to the issues of slow convergence speed,susceptibility to local optima,and low accuracy in fault diagnosis of photovoltaic arrays using back propagation neural network(BPNN),an improved sparrow search algorithm(ISSA-BP)for optimizing the weights and thresholds of the BP neural network has been proposed.Firstly,the Cubic chaotic mapping is employed to enhance the spatial coverage of the initial population positions.Subsequently,an inertia weight is introduced among the discoverers to accelerate convergence speed and strengthen local search capabilities.Finally,the diversity is maintained by dynamically adjusting the number of scouts,enhancing global search capabilities.The MATLAB/Simulink simulation model is utilized to obtain four feature parameters,namely short-circuit current,open-circuit voltage,maximum power point current,and maximum power point voltage,under normal and fault conditions in a photovoltaic array,which is input into six fault diagnosis models.Comparative verification with traditional BP,GA-BP,PSO-BP,SSA-BP,and SOA-SVM models is conducted.The experimental results demonstrate that the ISSA-BP model not only rapidly escapes local optima and accelerates convergence speed but also achieves a fault diagnosis accuracy of 97.5%.关键词
光伏阵列/故障诊断/反向传播神经网络/故障特征提取/改进麻雀搜索算法Key words
photovoltaic array/fault diagnosis/back propagation neural network/fault feature extraction/improved sparrow search algorithm分类
信息技术与安全科学引用本文复制引用
文力,谭功全,毛国斌,王旭东,庞宏杰..基于ISSA-BP神经网络的光伏阵列故障诊断方法[J].四川轻化工大学学报（自然科学版）,2025,38(1):57-68,12.基金项目
人工智能四川省重点实验室科研项目(2019RYJ08) （2019RYJ08）
