摘要
由于支持向量机中的参数会显著影响着支持向量机分类的精确度,建立了一种基于免疫算法优化最小二乘支持向量机的电力变压器故障诊断模型;该模型以变压器油中主要溶解气体作为向量机的输入,以变压器故障类型作为其相应的输出,选用径向基核、使用免疫算法得到优化参数,充分发挥向量机较高泛化能力的优势。实例验证表明,这种方法能提高变压器的故障诊断准确率,反映了其有效性和正确性。
Considering the fact that the parameter setting for support vector machine （SVM） impacts on the classification accuracy, a model for power transformer fault diagnosis based on least squares support vector machine（LS-SVM） of immune optimization was establishmented,in which the concentration of the characteristic gases dissolved in transformer oil are the inputs of support vector machine and fault types of the transformer are the outputs. In the model the radial basis kernel is selected,the optimized parameters are obtained using the immune algorithm, and the superiority of SVM in processing finite samples is fully brought into play.Simulation results show that the algorithm can detect transformer faults with a higher diagnosis rate, and prove the effectiveness and correctness of the method.
出处
《机械管理开发》
2012年第2期101-103,共3页
Mechanical Management and Development
关键词
故障诊断
电力变压器
免疫算法
最小二乘支持向量机
fault diagnosis
power transformer
immune algorithm
least squares support vector machine
