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.
REN Fang-yu (任方宇), SI Shu-bin* (司书宾), CAI Zhi-qiang (蔡志强), ZHANG Shuai (张帅)
. Transformer Fault Analysis Based on Bayesian Networks and Importance Measures[J]. Journal of Shanghai Jiaotong University(Science), 2015
, 20(3)
: 353
-357
.
DOI: 10.1007/s12204-015-1636-5
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