电力系统及其自动化学报2024，Vol.36Issue(8)：70-78,9.DOI:10.19635/j.cnki.csu-epsa.001317
改进蜣螂算法优化LSTM的光伏阵列故障诊断
Improved Dung Beetle Optimizer to Optimize LSTM for Photovoltaic Array Fault Diagnosis
摘要
Abstract
To improve the accuracy of photovoltaic(PV)array fault diagnosis,a PV array fault diagnosis method based on variational mode decomposition(VMD)and improved dung beetle optimizer(IDBO)is proposed to optimize the long short-term memory(LSTM)network. First,in response to the low convergence accuracy of the dung beetle optimizer (DBO)and its tendency to fall into local optima,an IDBO algorithm which integrates the Levy flight strategy,T-distri-bution perturbation strategy and multi-swarm mechanism is put forward. Through a comparison with the DBO,sparrow search algorithm,and whale optimization algorithm in optimization testing,the superiority of the IDBO algorithm is demonstrated. Afterwards,an IDBO-LSTM fault diagnosis model is constructed in combination with LSTM. Second,to fully explore the fault features,VMD is used to extract feature components at multiple levels of fault data as input to the IDBO-LSTM model. Finally,through a comparison among experimental results,it is shown that the fault diagnosis accu-racy of the proposed method reaches 98.34％ and is superior to those of the other five models,proving its feasibility and superiority.关键词
光伏阵列/改进蜣螂算法/变分模态分解/长短期记忆/故障诊断Key words
photovoltaic PV)array/improved dung beetle optimizer(IDBO)/variational mode decomposition(VMD)/long short-term memory(LSTM)/fault diagnosis分类
信息技术与安全科学引用本文复制引用
李斌,高鹏,郭自强..改进蜣螂算法优化LSTM的光伏阵列故障诊断[J].电力系统及其自动化学报,2024,36(8):70-78,9.基金项目
国家自然科学基金资助项目(51674136、52104160) （51674136、52104160）
