基于ISSA-SVM的充电桩故障诊断

Fault diagnosis of charging pile based on ISSA-SVM

针对充电桩故障诊断精度较低的问题,文中首先提出先利用多维尺度分析法处理样本数据,将原始数据映射到更低维的空间,减小模型计算代价;其次在麻雀搜索算法里融入了Sin混沌映射和动态自适应权重,提高它的全局搜索能力和寻优精度,然后再利用改进的麻雀搜索算法对支持向量机模型进行参数寻优,同时建立最优诊断模型;最后用所得模型进行充电桩故障诊断,输出诊断结果。最终的实验结果表明:文中提出的充电桩故障诊断模型的诊断准确率高达95.1351%,明显高于现有的一些常用模型。同时,文中所选用的支持向量机模型较其他分类模型效果更好,效率更高。

Aiming at the problem of low fault diagnosis accuracy of charging piles,this paper first proposes to use multi-dimensional scale analysis method to process sample data,map the original data to a lower dimensional space,and reduce the cost of model calculation.Secondly,Sin chaos mapping and dynamic adaptive weighting are integrated into the sparrow algorithm to improve its global search ability and optimization accuracy,and then the improved sparrow algorithm is used to optimize the parameters of the support vector machine model,and the optimal diagnosis model is established.Finally,the obtained model is used to diagnose the fault diagnosis of the charging pile and output the diagnosis results.The final experimental results show that the diagnostic accuracy of the fault-diagnosis model of the charging pile proposed in this paper is as high as 95.1351%,which is significantly higher than that of some existing commonly used models.At the same time,the support vector machine model selected in this paper has better effect and higher efficiency than other classification models.