基于邻域粗糙集与AMPOS-ELM的变压器DGA故障诊断
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1.国网宁夏电力有限公司电力科学研究院;2.宁夏电力能源科技有限公司;3.长沙理工大学电气与信息工程学院

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TM863

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国家自然科学基金项目(面上项目,重点项目,重大项目)(51407013)


Fault diagnosis of transformer DGA based on neighborhood rough set and AMPSO-ELM
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1.State Grid Ningxia Electric Power Co,Ltd Electric Power Research Institute;2.Ningxia Electric Power Energy Technology Co,Ltd;3.Changsha University of Science and Technology,School of Electrical and Information Engineering

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    摘要:

    基于DGA数据的智能变压器故障诊断方法准确率易受输入特征影响以及极限学习机参数选择困难的问题,提出基于邻域粗糙集与自适应变异粒子群极限学习机算法的变压器故障诊断方法。首先,结合各DGA故障诊断标准建立变压器故障初始特征集,采用邻域粗糙集分析后获得属性重要度高的关键特征指标;其次,针对粒子群算法优化极限学习机参数时容易早熟、陷入局部最优的缺陷,提出带有早熟自检变异机制的改进粒子群算法优化极限学习机;最后,通过变压器DGA数据实例诊断,将之与IEC三比值法以及不同组合的极限学习机诊断性能进行比较,表明所提方法诊断精度更高。

    Abstract:

    The accuracy of the intelligent transformer fault diagnosis method based on DGA data is easily affected by the input characteristics and the difficulty of parameter selection of the extreme learning machine. A transformer fault diagnosis method based on the neighborhood rough set and the adaptive mutation particle swarm extreme learning machine algorithm is proposed. First, the initial feature set of transformer faults is established based on the various DGA fault diagnosis standards, and the key feature indicators with high attribute importance are obtained after the neighborhood rough set analysis; secondly, when optimizing the parameters of the extreme learning machine for the particle swarm algorithm, it is easy to be premature and fall into the local maximum. For excellent defects, an improved particle swarm optimization algorithm with premature self-check mutation mechanism is proposed to optimize the extreme learning machine; finally, through the transformer DGA data example diagnosis, it is compared with the IEC three-ratio method and the diagnostic performance of different combinations of extreme learning machines. It shows that the diagnosis accuracy of the proposed method is higher.

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历史
  • 收稿日期:2020-10-13
  • 最后修改日期:2020-11-16
  • 录用日期:2020-11-23
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