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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Foundation item: the National Natural Science Foundation of China (Nos. 71271170 and 71471147), the Program for New Century Excellent Talents in University (No. NCET-13-0475) and the China Aeronautical Science Foundation (No. 2014ZG53080)
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Ren, Fy., Si, Sb., Cai, Zq. et al. Transformer fault analysis based on Bayesian networks and importance measures. J. Shanghai Jiaotong Univ. (Sci.) 20, 353–357 (2015). https://doi.org/10.1007/s12204-015-1636-5
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DOI: https://doi.org/10.1007/s12204-015-1636-5