| 基于KPCA-WPA-SVM的变压器故障诊断模型 |
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| 引用本文： | 陈铁,吕长钦,张欣,陈卫东.基于KPCA-WPA-SVM的变压器故障诊断模型[J].电测与仪表,2021,58(4):158-164. |
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| 作者姓名： | 陈铁 吕长钦 张欣 陈卫东 |
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| 作者单位： | 三峡大学水电站运行与控制湖北省重点实验室,湖北宜昌443000;三峡大学电气与新能源学院,湖北宜昌443000;广西电网有限责任公司柳州供电局,广西柳州545005 |
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| 基金项目： | 国家自然科学基金资助项目 |
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| 摘 要： | 为提高变压器故障诊断的精度,文章提出一种基于核主成分分析(KPCA)和狼群算法(WPA)优化支持向量机(SVM)参数的变压器故障诊断方法。通过KPCA提取样本数据的非线性特征,并获得其主成分,再将其输入至高斯核SVM构成诊断模型,并利用WPA对SVM的惩罚因子以及核参数进行优化。实验结果表明,该方法诊断准确率达到93.33%,与传统SVM以及KPCA-SVM诊断模型相对比,具有更高的变压器故障诊断准确率。
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| 关 键 词： | 核主成分分析 狼群算法 支持向量机 故障诊断 电力变压器 |
| 收稿时间： | 2021/1/13 0:00:00 |
| 修稿时间： | 2021/1/19 0:00:00 |
Transformer Fault Diagnosis Model Based on KPCA-WPA-SVM |
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| chen tie,lv changqin,zhang xin and chenweidong.Transformer Fault Diagnosis Model Based on KPCA-WPA-SVM[J].Electrical Measurement & Instrumentation,2021,58(4):158-164. |
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| Authors: | chen tie lv changqin zhang xin and chenweidong |
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| Affiliation: | (Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station,China Three Gorges University,Yichang 443000,Hubei,China;School of Electrical Engineering and New Energy,China Three Gorges University,Yichang 443000,Hubei,China;Liuzhou Power Supply Bureau,Guangxi Power Grid Co.,Ltd.,Liuzhou 545005,Guangxi,China) |
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| Abstract: | In order to improve the accuracy of transformer fault diagnosis,this paper proposes a transformer fault diagnosis method based on kernel principal component analysis(KPCA)and wolf pack algorithm(WPA)to optimize the support vector machine(SVM)parameters.KPCA is used to extract the non-linear characteristics of the sample data and obtains its principal components,and then,inputs them into the Gaussian kernel SVM to form a diagnostic model,and the wolf pack algorithm(WPA)is used to optimize the penalty factor and kernel parameters of the SVM.Experimental results show that the diagnostic accuracy rate of the proposed method reaches 93.33%,which is higher than that of the traditional support vector machine and KPCA-SVM diagnostic model. |
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| Keywords: | KPCA WPA SVM fault diagnosis transformer |
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