| 引用本文: | 司马莉萍,舒乃秋,李自品,黄 勇,罗晓庆.基于SVM和D-S证据理论的电力变压器内部故障部位识别[J].电力自动化设备,2012,32(11): |
| SIMA Liping,SHU Naiqiu,LI Zipin,HUANG Yong,LUO Xiaoqing.Identification of interior fault position based on SVM and D-S evidence theory for electric transformer[J].Electric Power Automation Equipment,2012,32(11): |
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| 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。 |
| 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 |
| DOI： |
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| 基金项目:国家自然科学基金资助项目（51207113） |
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| Identification of interior fault position based on SVM and D-S evidence theory for electric transformer |
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SIMA Liping, SHU Naiqiu, LI Zipin, HUANG Yong, LUO Xiaoqing
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School of Electrical Engineering，Wuhan University，Wuhan 430072，China
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| Abstract: |
| Since it is difficult to determine the BPA（Basic Probability Assignment） in the transformer infor-mation 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（Dissovled 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. |
| Key words: electric transformers support vector machines D-S evidence theory posterior probability fault position identification models |
