| 引用本文: | 方 涛,钱 晔,郭灿杰,等.基于天牛须搜索优化支持向量机的变压器故障诊断研究[J].电力系统保护与控制,2020,48(20):90-96.[点击复制] |
| FANG Tao,QIAN Ye,GUO Canjie,et al.Research on transformer fault diagnosis based on a beetle antennae search optimized support vector machine[J].Power System Protection and Control,2020,48(20):90-96[点击复制] |
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| 摘要: |
| 为了准确地判断变压器绕组是否出现故障，保证变压器供电的可靠性，提出了一种基于天牛须搜索算法优化支持向量机(BAS-SVM)的变压器绕组故障诊断方法。采用支持向量机(SVM)作为变压器绕组形变程度的分类器，并应用天牛须算法对SVM的核函数和惩罚因子进行优化，通过人工经验训练BAS-SVM，使其具有很高的故障诊断精度。为了比较BAS-SVM算法在变压器绕组故障诊断的优越性，采用改进的粒子群优化算法(MPSO)优化SVM。通过仿真验证，BAS-SVM算法的故障诊断准确率比MPSO-SVM算法的故障诊断准确率高10%。最后通过实例验证了BAS-SVM算法对变压器绕组故障诊断的可行性。 |
| 关键词: 变压器 故障诊断 BAS-SVM 绕组变形 MPSO-SVM |
| DOI：DOI: 10.19783/j.cnki.pspc.191534 |
| 投稿时间：2019-12-12修订日期：2020-02-06 |
| 基金项目:国家电网公司总部科技项目资助(52170218000M)；国网河南省电力公司2019年科技项目资助(5217A01801U5) |
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| Research on transformer fault diagnosis based on a beetle antennae search optimized support vector machine |
| FANG Tao,QIAN Ye,GUO Canjie,SONG Chuang,WANG Zhihua,LUO Jianping,BA Quanke |
| (1. Luoyang Power Supply Company, State Grid Henan Electric Power Company, Luoyang 471000, China; 2. State Grid Henan
Electric Power Research Institute, Zhengzhou 450052, China; 3. Wuhan Kemov Electric Co., Ltd., Wuhan 430023, China) |
| Abstract: |
| In order to accurately judge whether a transformer winding has faults and ensure the reliability of power supply of the transformer, a method of transformer winding fault diagnosis based on BAS-SVM is proposed. It uses an SVM as the classifier of the transformer winding deformation degree, and optimizes the kernel function and penalty factor of the SVM by using a beetle antennae search algorithm. The BAS-SVM is trained by artificial experience to ensure that the algorithm has a high accuracy of fault diagnosis. In order to compare the advantages of the BAS-SVM algorithm in this application, a Modified Particle Swarm Optimization (MPSO) is also used to optimize SVM. The simulation results show that the fault diagnosis accuracy rate of the BAS-SVM algorithm is 10% higher than that of MPSO-SVM algorithm. Finally, the effectiveness of the BAS-SVM method on transformer winding fault diagnosis is verified by an example.
This work is supported by Science and Technology Project of the Headquarter of State Grid Corporation of China (No. 52170218000M) and Science and Technology Project of State Grid Henan Electric Power Company in 2019 (No. 5217A01801U5). |
| Key words: transformer fault diagnosis BAS-SVM winding deformation MPSO-SVM |
