为提升光伏阵列故障诊断的准确率,提出利用柯西高斯变异人工兔算法（CGARO）来优化长短期记忆网络（LSTM）的策略,以实现对光伏阵列多类型复合故障的高效诊断。首先,为改善人工兔优化（ARO）算法易陷入局部最优的问题,提出一种柯西高斯变异人工兔算法。将CGARO与ARO、麻雀搜索算法（SSA）、灰狼优化算法（GWO）进行对比分析,验证CGARO算法的有效性。然后,将CGARO算法优化LSTM的参数及学习率,建立CGARO-LSTM光伏阵列故障诊断模型,基于4种单一故障和3种多类型复合故障,通过与LSTM、SSA-LSTM、GWO-LSTM和ARO-LSTM进行对比,表明CGARO-LSTM模型具有更优性能,准确率达到97.75%,显著提高了光伏阵列故障诊断的精度。
Abstract
To improve the accuracy of fault diagnosis in photovoltaic arrays, a strategy is proposed to optimize the Long Short-Term Memory network （LSTM） using the Cauchy Gaussian Mutation Artificial Rabbit Algorithm （CGARO） to achieve efficient diagnosis of multiple types of composite faults in photovoltaic arrays. Firstly, in order to the problem of getting trapped in local optima in the artificial rabbit optimization （ARO）, a Cauchy Gaussian mutation artificial rabbit algorithm is proposed. Comparative analysis was conducted between CGARO and ARO, Sparrow Search Algorithm （SSA）, and Grey Wolf Optimization Algorithm（GWO） to verify the effectiveness of CGARO algorithm. Then, the CGARO algorithm was optimized to optimize the parameters and learning rate of LSTM, and a CGARO-LSTM photovoltaic array fault diagnosis model was established. Based on four types of single faults and three types of composite faults, the CGARO-LSTM model was compared with LSTM, SSA-LSTM, GWO-LSTM, and ARO-LSTM. The results showed that the CGARO-LSTM model had better performance, with an accuracy of 97.75%, significantly improving the accuracy of photovoltaic array fault diagnosis.
关键词
光伏阵列 /
长短期记忆网络 /
进化算法 /
故障诊断 /
诊断模型 /
故障类型 /
准确率
Key words
photovoltaic arrays /
LSTM /
evolutionary algorithm /
fault diagnosis /
diagnosis model /
type of faults /
accuracy
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