基于贝叶斯网络和假设检验的变压器故障诊断
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通讯作者:

何宁辉(1986-),男,博士,高级工程师,主要从事电力设备状态监测技术研究;E-mail:232464433@qq.com

中图分类号:

TM86

基金项目:

国网宁夏电力有限公司重点研发计划(2020YCYF0112)


Transformer fault diagnosis based on bayesian network and hypothesis testing
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    摘要:

    电力变压器故障的准确诊断对于电网的可靠运行至关重要。为此,提出一种基于贝叶斯网络和假设检验的溶解气体分析的新型多分类概率诊断模型。该贝叶斯网络模型可嵌入专家知识,从数据中学习数据模式并推断与诊断结果相关的不确定性,并且通过假设检验环节改进数据的选择过程。最后,基于IECTC10数据集,对比3种传统诊断方法进行诊断实验,验证所提出模型的有效性。结果表明提出的诊断模型最大诊断准确性为88.9%,相比传统诊断方法有较大提升。

    Abstract:

    Accurate diagnosis of power transformer faults is essential to the reliable operation of the power grid. To achieve this goal, this paper proposes a new multiclass probabilistic diagnostic model based on bayesian network and hypothesis testing of dissolved gas analysis. The bayesian network model can embed expert knowledge, learn data patterns from data and infer uncertainties related to diagnosis results, and improve the data selection process through hypothesis testing. Based on the IEC TC10 data set, this paper compares three traditional diagnostic methods to perform diagnostic experiments to verify the effectiveness of the proposed model. The results show that the maximum diagnostic accuracy of the proposed diagnostic model is 88.9%, which is greatly improved compared to traditional diagnostic methods.

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何宁辉,朱洪波,李秀广,等.基于贝叶斯网络和假设检验的变压器故障诊断[J].电力科学与技术学报,2021,36(6):20-27.
HE Ninghui, ZHU Hongbo, LI Xiuguang, et al. Transformer fault diagnosis based on bayesian network and hypothesis testing[J]. Journal of Electric Power Science and Technology,2021,36(6):20-27.

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  • 在线发布日期: 2022-01-05
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