| Transformer Fault Analysis Based on Bayesian Networks and Importance Measures |
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| 作者姓名： | 任方宇 司书宾 蔡志强 张帅 |
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| 作者单位： | School of Mechantronics, Northwestern Polytechnical University |
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| 摘 要： | Complex environment stresses bring many uncertainties to transformer fault. The Bayesian network(BN) can represent prior knowledge in the form of probability which makes it an effective tool to deal with the uncertain problems. This paper established a BN model for the transformer fault diagnosis with practical operation dataset and expert knowledge. Then importance measures are introduced to indentify the key attributes which affect the results of transformer diagnosis most. Moreover, a strategy was proposed to reduce the number of attribute in transformer fault detection and the resource cost was saved. At last, a diagnosis case of practical transformer was implemented to verify the effectiveness of this method.
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| 关 键 词： | Bayesian transformer attribute prior verify uncertain correspondence classifier appearance True |
Transformer Fault Analysis Based on Bayesian Networks and Importance Measures |
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| REN Fang-yu
,
SI Shu-bin
,
CAI Zhi-qiang
,
ZHANG Shuai.Transformer Fault Analysis Based on Bayesian Networks and Importance Measures[J].Journal of Shanghai Jiaotong university,2015,20(3):353-357. |
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| Authors: | REN Fang-yu SI Shu-bin CAI Zhi-qiang ZHANG Shuai |
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| Institution: | School of Mechantronics, Northwestern Polytechnical University, Xi'an 710072, China |
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| Abstract: | Complex environment stresses bring many uncertainties to transformer fault. The Bayesian network(BN) can represent prior knowledge in the form of probability which makes it an effective tool to deal with the uncertain problems. This paper established a BN model for the transformer fault diagnosis with practical operation dataset and expert knowledge. Then importance measures are introduced to indentify the key attributes which affect the results of transformer diagnosis most. Moreover, a strategy was proposed to reduce the number of attribute in transformer fault detection and the resource cost was saved. At last, a diagnosis case of practical transformer was implemented to verify the effectiveness of this method. |
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| Keywords: | transformer fault diagnosis Bayesian network (BN) importance measures |
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