电能计量装置故障诊断中ISOA-SVM算法实现

随着用电需求增大，电能计量装置的可靠性与安全性备受关注。针对电能计量装置的故障诊断正确率低的难题，研究设计了一种改进海鸥算法优化支持向量机（ISOA-SVM）模型。为弥补海鸥优化算法（SOA）的不足，提出了寻优性能较好的改进海鸥优化算法（ISOA）。采用ISOA优化SVM的内部参数，构建了基于ISOA-SVM算法的电能计量装置的故障诊断模型。实验结果为在相同评价指标下，ISOA-SVM模型的50次故障诊断的平均值高达96.575%，较PSO-SVM、SOA-SVM、SVM及ELM模型的故障诊断正确率提高了6.681%、5.63%、11.95%和12.79%，表明研究所设计的ISOA-SVM算法鲁棒性强，故障诊断性能良好。

Implementation of ISOA-SVM Algorithm in Fault Diagnosis of Electric Energy Metering Device

With the increasing demand for electricity, the reliability and safety of electric energy metering devices have attracted much attention. Aiming at the problem of low accuracy of fault diagnosis of electric energy metering device, an improved seagull algorithm optimized support vector machine (ISOA-SVM) model is studied and designed. In order to make up for the deficiency of seagull optimization algorithm (SOA), an improved seagull optimization algorithm (ISOA) with better optimization performance is proposed. The internal parameters of SVM are optimized by ISOA, and the fault diagnosis model of electric energy metering device based on ISOA-SVM algorithm is constructed. The experimental results show that under the same evaluation index, the average value of 50 fault diagnosis of ISOA-SVM model is as high as 96.575%, which is 6.681%, 5.63%, 11.95% and 12.79% higher than that of PSO-SVM, SOA-SVM, SVM and ELM model. It shows that the designed ISOA-SVM algorithm has strong robustness and good fault diagnosis performance.