应用贝叶斯框架的LS-SVM概率输出诊断电力变压器故障

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。

Fault Diagnosis Method for Power Transformer Based on LS-SVM Probability Output of Bayesian Framework

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.