基于改进海鸥算法优化SVM的变压器故障诊断方法
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

TM411

基金项目:

新疆维吾尔自治区重大科技专项(2022A01001-4)


Research on Transformer Fault Diagnosis Method Based on Improved Seagull Algorithm Optimized SVM
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    变压器故障诊断率不足一直是制约着电网运行安全和效率低下的关键问题。为解决这一问题,本文提出了基于改进海鸥算法优化SVM(Improved Seagull Optimization Algorithm Support Vector Machine,ISOA-SVM)的变压器故障诊断方法。首先开始构建SVM的油中溶解气体分析的故障诊断模型并通过核主成分(kernel principal component analysis KPCA)对油中数据处理;其次通过ISOA寻找到SVM的最优核函数参数和惩罚系数;最后将数据归一化输入ISOA-SVM模型进行诊断,判断变压器的运行状态,并将结果与其他算法优化模型进行比较,仿真结果显示,该模型故障检测方法在识别故障速度以及识别精度上明显优于其他模型,有助于保证变压器的稳定运行。

    Abstract:

    Insufficient transformer fault diagnosis rate has always been a key problem restricting the safety and low efficiency of power grid operation. To solve this problem, a transformer fault diagnosis method based on Improved Seagull Optimization Algorithm Support Vector Machine (ISOA-SVM) is proposed in this paper. Firstly, the fault diagnosis model of dissolved gas analysis in oil based on SVM is constructed, and the data in oil is processed by Kernel Principal Component Analysis (KPCA). Secondly, the optimal kernel function parameters and penalty coefficient of SVM are found by ISOA. Finally, the data is normalized into the ISOA-SVM model for diagnosis, and the operational state of the transformer is judged. The results are compared with other algorithm optimization models. The simulation results show that the fault detection method of the model is significantly superior to other models in fault identification speed and accuracy, which helps to ensure the stable operation of the transformer.

    参考文献
    相似文献
    引证文献
引用本文

时宇辉,袁至,王维庆,等. 基于改进海鸥算法优化SVM的变压器故障诊断方法[J]. 科学技术与工程, 2024, 24(28): 12169-12176.
shiyuhui, yuanzhi, wangweiqing, et al. Research on Transformer Fault Diagnosis Method Based on Improved Seagull Algorithm Optimized SVM[J]. Science Technology and Engineering,2024,24(28):12169-12176.

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2024-02-26
  • 最后修改日期:2024-08-06
  • 录用日期:2024-03-21
  • 在线发布日期: 2024-11-05
  • 出版日期: