变压器故障诊断率不足一直是制约着电网运行安全和效率低下的关键问题.为解决这一问题,提出基于改进海鸥算法优化支持向量机(improved seagull optimization algorithm support vector machine,ISOA-SVM)的变压器故障诊断方法.首先开始构建SVM的油中溶解气体分析的故障诊断模型并通过核主成分分析(kernel principal component analysis,KPCA)对油中数据处理;其次通过ISOA寻找到SVM的最优核函数参数和惩罚系数;最后将数据归一化输入ISOA-SVM模型进行诊断,判断变压器的运行状态,并将结果与其他算法优化模型进行比较,仿真结果显示,该模型故障检测方法在识别故障速度以及识别精度上明显优于其他模型,有助于保证变压器的稳定运行.
Transformer Fault Diagnosis Method Based on Improved Seagull Algorithm Optimized SVM
Insufficient transformer fault diagnosis rate has always been a key problem restricting the safety and low efficiency of power grid operation.To solve this problem,a transformer fault diagnosis method based on improved seagull optimization algorithm support vector machine(ISOA-SVM)was proposed.Firstly,the fault diagnosis model of dissolved gas analysis in oil based on SVM was constructed,and the data in oil was processed by kernel principal component analysis(KPCA).Secondly,the optimal kernel function parameters and penalty coefficient of SVM were found by ISOA.Finally,the data was normalized into the ISOA-SVM model for diagnosis,and the operational state of the transformer was judged.The results were compared with other algorithm optimization models.The simulation results show that the fault detection method of the model is significantly superior to other models in fault identification speed and accuracy,which helps to ensure the stable operation of the transformer.
transformerkernel principal component analysis(KPCA)support vector machine(SVM)optimized seagull algorithmfault diagnosis
万方数据
维普
