基于MPC算法优化的贝叶斯网络变压器故障诊断
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河南理工大学 电气工程与自动化学院 河南 焦作 454003

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TP183

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国家自然科学基金(U1504623)资助项目


Transformer fault diagnosis based on MPC algorithm optimized by bayesian network
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School of Electrical Engineering & Automation, Henan Polytechnic University, Jiaozuo 454003, China

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    摘要:

    为提高变压器故障诊断的准确率及可靠性,提出了基于MPC(modification of the PC,简称MPC)算法优化贝叶斯网络的变压器故障诊断方法,对变压器故障诊断技术进行了研究。首先,根据油中溶解气体分析,采用无编码比值法提取油浸式变压器的9维故障特征,并对数据样本进行归一化处理;其次,以归一化的训练样本作为输入建立基于贝叶斯网络的故障诊断模型,采用MPC算法对贝叶斯网络模型进行优化;最后,利用测试样本对故障诊断模型进行测试。为了证明所提出方法的优越性,将本文研究方法与现有故障诊断方法进行了对比。结果表明,所提出方法的诊断正确率更高,诊断效果更好。

    Abstract:

    In order to improve the accuracy and reliability of the transformer fault diagnosis, a fault diagnosis method of transformer based on MPC(modification of the PC, for short: MPC) algorithm optimized by Bayesian network was proposed, and the fault diagnosis technology of transformer was studied. Firstly, according to the analysis of dissolved gas in oil, the 9-D fault features of oil-immersed transformer were extracted by the non-coding ratio method, and the data samples were normalized. Secondly, a fault diagnosis model based on Bayesian network was established with normalized training samples as input, and the Bayesian network model was optimized with the MPC algorithm. Finally, the fault diagnosis model was tested with test samples. In order to prove the superiority of the proposed method, the proposed method was compared with the existing fault diagnosis methods. The result shows that the proposed method has higher diagnostic accuracy and better diagnostic effect.

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仝兆景,乔征瑞,李金香,兰孟月,荆利菲.基于MPC算法优化的贝叶斯网络变压器故障诊断[J].电子测量技术,2021,44(17):41-45

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  • 在线发布日期: 2024-08-09
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