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| 引用本文: | 薛浩然,张珂珩,李斌,等.基于布谷鸟算法和支持向量机的变压器故障诊断[J].电力系统保护与控制,2015,43(8):8-13.[点击复制] |
| XUE Haoran,ZHANG Keheng,LI Bin,et al.Fault diagnosis of transformer based on the cuckoo search and support vector machine[J].Power System Protection and Control,2015,43(8):8-13[点击复制] |
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| 基于布谷鸟算法和支持向量机的变压器故障诊断 |
| 薛浩然,张珂珩,李斌,彭晨辉 |
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| (国网电力科学研究院/南京南瑞集团公司，江苏 南京 210003;江苏瑞中数据
股份有限公司，江苏 南京 210003) |
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| 摘要: |
| 电力变压器是电力系统运行中的重要设备之一，对故障和缺陷进行正确的诊断，关系到整个电网的运行安全。支持向量机(SVM)能够较好地解决小样本、非线性特征的多分类问题，适用于变压器故障类型判断。利用布谷鸟搜索算法，对支持向量机进行寻优得到全局最优解，从而得到具有最佳参数的支持向量机分类模型。该分类模型将变压器油色谱数据(DGA)中各气体相对含量作为评估指标，将变压器的故障分为低能放电、高能放电、中低温过热、高温过热等4个故障类型。通过已有的数据实例分析得出，利用布谷鸟搜索算法得到的分类模型比常用的网格搜索算法(GS)、粒子群搜索算法(PSO)、遗传算法搜索(GA)等算法得到的模型拟合准确率更好。 |
| 关键词: 支持向量机 布谷鸟算法 变压器 故障诊断 分类模型 |
| DOI：10.7667/j.issn.1674-3415.2015.08.002 |
| 投稿时间：2014-07-04 |
| 基金项目: |
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| Fault diagnosis of transformer based on the cuckoo search and support vector machine |
| XUE Haoran,ZHANG Keheng,LI Bin,PENG Chenhui |
| (NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 210003, China;China Real Time Database Co., Ltd., Nanjing 210003, China) |
| Abstract: |
| Power transformer is one of the important equipment in power system operation, correct diagnosis of the fault and defects is related to the safe operation of the entire grid. Support vector machine(SVM) can better solve the multi-classification with small sample and nonlinear characteristics, it is suitable for fault diagnosis of transformer. In this paper, we get the best global optimal solution of SVM using the cuckoo search algorithm, and get the SVM classification model with the best parameters. In this classification model, the relative content of each gas of dissolved gas analysis (DGA) is put as the evaluation indexes. The transformer fault is divided into 4 types of fault, i.e. low energy discharge, high energy discharge, mid-low temperature overheating, and high temperature overheating. Through the analysis of the existing data instance, the accuracy of the classification model using cuckoo search algorithm is better than that using grid search (GS), particle swarm optimization (PSO) and genetic algorithm (GA). |
| Key words: support vector machine (SVM) cuckoo search transformer fault diagnosis classification model |
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