引用本文: | 程加堂,艾莉,段志梅.改进证据理论与神经网络集成的变压器故障诊断[J].电力系统保护与控制,2013,41(14):92-96.[点击复制] |
CHENG Jia-tang,AI Li,DUAN Zhi-mei.Transformer fault diagnosis based on improved evidence theory and neural network integrated method[J].Power System Protection and Control,2013,41(14):92-96[点击复制] |
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摘要: |
针对变压器故障类型的多样性以及故障信息的不确定性等问题,提出了证据理论与神经网络综合集成的故障诊断方法。为实现Dempster合成规则在强冲突证据间信息融合后可信度分配的合理赋值,引入了信任系数的概念,对融合结果进行修正,并应用于最大-最小蚂蚁系统与神经网络算法所形成证据体的合成之中。实验仿真结果表明,该方法可以在初级诊断模块的判断结果出现严重分歧的情况下,仍得到较好的符合性判定结论,从而实现对变压器故障的有效诊断。 |
关键词: 变压器 证据理论 合成规则 故障诊断 |
DOI:10.7667/j.issn.1674-3415.2013.14.015 |
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基金项目:云南省教育厅科学研究基金项目(2012Y450) |
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Transformer fault diagnosis based on improved evidence theory and neural network integrated method |
CHENG Jia-tang,AI Li,DUAN Zhi-mei |
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Abstract: |
Considering the diversity of the transformer fault types and fault information uncertainty, the paper proposes the fault diagnosis method based on the combination of evidence theory and neural network. In order to realize the reasonable assignment of reliability by Dempster combination rule after the information fusion between strong conflict evidence, the concept of a trust coefficient is introduced to correct fusion results and is used in the synthesis of max-min ant system and neural network algorithm which form the body of evidence. Simulation results show that the method can still get better compliance determination result when the results of the initial diagnostic module is seriously divided, so it achieves effective transformer fault diagnosis. |
Key words: transformer evidence theory combination rule fault diagnosis |