摘要
针对变压器信息融合诊断方法中难以确定基本概率分配(BPA)的缺陷,提出一种基于多支持向量机(SVM)与D-S证据理论的变压器内部故障部位识别模型。利用"一对一"多类SVM后验概率估计分配BPA,实现其赋值的客观化;充分利用变压器油中溶解气体分析数据和电气试验数据的互补信息,对变压器内部可能发生故障的部位进行诊断。实例分析表明,所提模型能有效识别故障部位,在准确率和泛化性方面都较单特征的SVM有优势。
Since it is difficult to determine the BPA（Basic Probability Assignment） in the transformer information fusion diagnosis method,an identification model based on the multi-SVM（Support Vector Machine） and D-S evidence theory is proposed for the interior fault position of electric transformer.The BPA is objectively realized based on the ＂one-versus-one＂ multi-class SVM posterior probability estimation;the complementary information of DGA（Dissolved Gas Analysis） data and routine electrical test data is fully utilized to detect the possible interior fault positions of electric transformer.Practical examples show that,the proposed model identifies the fault positions effectively and is better than the mono-SVM in both accuracy and generalization.
出处
《电力自动化设备》
EI
CSCD
北大核心
2012年第11期72-77,共6页
Electric Power Automation Equipment
基金
国家自然科学基金资助项目(51207113)~~
关键词
变压器
支持向量机
D-S证据理论
后验概率
故障部位识别
模型
electric transformers
support vector machines
D-S evidence theory
posterior probability
fault position identification
models
