摘要
针对传统最小二乘支持向量机(LS-SVM)分类器的参数选择具有随意性和不确定性等不足,采用贝叶斯推断方法、通过3级分层推断优化来确定最小二乘支持向量机的各参数,有效提高了最小二乘支持向量机的建模效率。结合最小二乘支持向量机的后验概率输出,可将其运用到变压器故障诊断中。仿真结果表明:该方法能有效地诊断电力变压器故障,且诊断精度和建模效率均优于传统的最小二乘支持向量机方法。
In order to remedy the randomness and uncertainty in selection process,the parameters of the least squares support vector machines （LS-SVM） classifier are optimally selected by the Bayesian inference with three levels hierarchy which can significantly improves modeling efficiency.Combined with probability outputs of multiclass LS-SVMS,the Bayesian inference LS-SVM classification method is applied to diagnose the power transformer fault diagnosis.The experimental simulation results show that the proposed approach can identify faults successfully.Both the diagnosis accuracy and modeling efficiency are better than traditional LS-SVM method.
出处
《中国电力》
CSCD
北大核心
2015年第5期41-45,共5页
Electric Power
基金
甘肃省自然科学基金资助项目(1310RJZA038)~~
关键词
变压器
故障诊断
最小二乘支持向量机
参数选择
建模效率
诊断精度
贝叶斯推断
概率输出
power transformer
fault diagnosis
least squares support vector machines
parameters
modeling
diagnosis accuracy
bayesian inference
probability outputs
