基于FASSA-SVM的充电桩故障预测算法研究

Research on fault prediction algorithm of charging pile based on FASSA-SVM

为了电动汽车直流充电桩的安全稳定运行,提出一种基于改进支持向量机的充电桩故障预测算法。该算法首先针对充电桩的运行参数进行缺失值填充、归一化等预处理;然后将预处理后的数据输入支持向量机模型训练,之后引入萤火虫算法改进麻雀算法对支持向量机模型进行参数寻优,得到最优模型;最后利用得到的最优模型预测诊断充电桩运行状态,来判断充电桩是否发生故障。实验结果表明,本文的预测算法预测精度可达94.68%,远高于传统的支持向量机模型的72.34%,能较准确地预测充电桩运行状态,为其预知维修、保障安全运行提供有力保障。

For the safe and stable operation of electric vehicle DC charging piles,this paper proposes a charging pile fault prediction algorithm based on improved support vector machine.The algorithm first performs preprocessing such as missing value filling and normalization in the operating parameters of the charging pile;then the preprocessed data is input into the support vector machine model for training,and then the firefly algorithm is introduced for improving the sparrow algorithm to search for the parameters for the support vector machine model.The optimal model is obtained;finally,the obtained optimal model is using to predict and diagnose the operation state for the charging pile to do judge whether the charging pile is faulty.The experimental results show that the prediction accuracy of the prediction algorithm in this paper could reach 94.68%,which is much higher than 72.34%of the traditional support vector machine model.