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PSO-IGWO优化混合KELM的变压器故障诊断方法

The transformer fault diagnosis method based on PSO-IGWO optimized hybrid KELM

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【作者】 王享黄新波朱永灿

【Author】 WANG Xiang;HUANG Xinbo;ZHU Yongcan;School of Electronics and Information, Xi′an Polytechnic University;

【通讯作者】 黄新波;

【机构】 西安工程大学电子信息学院

【摘要】 对变压器进行智能化故障诊断是促进智能电网发展的主要环节,但传统的单一智能诊断算法不能对变压器的大量不完备信息进行有效处理,导致故障诊断精度不高。为此,给出一种基于粒子群混合改进灰狼算法(PSO-IGWO)优化混合核极限学习机(KELM,kernal extreme learning machine)的变压器故障诊断方法。通过混合KELM建立故障诊断模型,采用粒子群算法对混合KELM的结构参数进行寻优,利用改进灰狼算法在局部和全局之间良好的平衡能力改善粒子群算法的缺陷,结合油中溶解气体分析(DGA,dissolved gas analysis)样本数据进行仿真实验。结果表明,相对于BPNN,ELM算法,诊断准确率分别提高了16.24%,5.71%,能够为变压器的安全稳定运行提供决策支持。

【Abstract】 Intelligent fault diagnosis of transformer is the main link to promote the development of the smart grid. However, the traditional single intelligent diagnosis algorithm can not deal with a large amount of incomplete information of transformer, resulting in low accuracy of fault diagnosis. Therefore, a transformer fault diagnosis method based on improved gray wolf optimization algorithm(PSO-IGWO) with optimized hybrid kernel extreme learning machine(KELM) was proposed. The fault diagnosis model was established by the hybrid KELM, and the structural parameters of hybrid KELM were optimized by particle swarm optimization algorithm. The simulated experiment was made, combined with the dissolved gas analysis(DGA) sample data. The results show that compared with BPNN and ELM, the diagnostic accuracy of this algorithm is improved by 16.24% and 5.71% respectively, which can provide decision support for the safe and stable operation of transformers.

【基金】 陕西省重点研发计划项目(2018ZDXM-GY-040)
  • 【文献出处】 西安工程大学学报 ,Journal of Xi’an Polytechnic University , 编辑部邮箱 ,2019年02期
  • 【分类号】TM407;TP18
  • 【网络出版时间】2019-05-06 16:38
  • 【被引频次】9
  • 【下载频次】209
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