上海交通大学学报(英文版) ›› 2015, Vol. 20 ›› Issue (3): 353-357.doi: 10.1007/s12204-015-1636-5
REN Fang-yu (任方宇), SI Shu-bin* (司书宾), CAI Zhi-qiang (蔡志强), ZHANG Shuai (张帅)
出版日期:
2015-06-27
发布日期:
2015-06-11
通讯作者:
SI Shu-bin (司书宾)
E-mail:sisb@nwpu.edu.cn
REN Fang-yu (任方宇), SI Shu-bin* (司书宾), CAI Zhi-qiang (蔡志强), ZHANG Shuai (张帅)
Online:
2015-06-27
Published:
2015-06-11
Contact:
SI Shu-bin (司书宾)
E-mail:sisb@nwpu.edu.cn
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.
中图分类号:
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]. 上海交通大学学报(英文版), 2015, 20(3): 353-357.
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.
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