基于改进蜣螂算法优化CNN-BiLSTM-Attention的串联电弧故障检测方法

Series Arc Fault Detection Based on Improved Dung Beetle Optimizer Optimized CNN-BiLSTM-Attention

针对故障电弧特征提取不足、检测精度不高等问题,提出一种多特征融合的改进蜣螂算法(IDBO)优化融合注意力(Attention)机制的卷积神经网络(CNN)和双向长短期记忆(BiLSTM)神经网络的串联电弧故障检测方法。通过实验平台提取电流的时域、频域、时频域以及信号自回归参数模型特征;利用核主成分分析(KPCA)对特征进行降维融合,并将求取的特征向量作为CNN-BiLSTM-Attention的输入向量;引入Cubic混沌映射、螺旋搜索策略、动态权重系数、高斯柯西变异策略对蜣螂算法进行改进,利用改进蜣螂算法对CNN-BiLSTM-Attention超参数优化实现串联电弧故障诊断。结果表明,所提方法故障电弧检测准确率达到97.92%,可高效识别串联电弧故障。

Aiming at the problems of insufficient arc fault feature extraction and low detection accuracy,a multi-feature fusion improved dung beetle optimizer(IDBO)optimized fusion of the attention mechanism of convolutional neural network(CNN)and bidirectional long short term memory(BiLSTM)neural network series arc fault detection method is proposed.The current time-domain,frequency-domain,time-frequency domain,and signal autoregressive parameter model features are extracted through an experimental platform.The kernel principal component analysis(KPCA)is used to reduce the dimensionality of the features,and they are fused to obtain the feature vectors as the input vectors for CNN-BiLSTM-Attention.The cubic chaotic mapping,the spiral search strategy,the dynamic weight coefficients,and the gaussian cauchy mutation strategy are introduced to improve the dung beetle optimizer.An improved dung beetle optimizer is used to optimize the hyperparameters of CNN-BiLSTM-Attention for the series arc fault diagnosis.The results show that the proposed method can achieve an accuracy of 97.92%in detecting fault arcs and efficiently identify the series arc faults.