| 基于粒子群优化支持向量机的变压器故障诊断 |
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| 引用本文： | 朱文俊,周〓刚,王红斌,尹玉娟,张金江,郭创新.基于粒子群优化支持向量机的变压器故障诊断[J].水电能源科学,2012,30(4):179-182. |
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| 作者姓名： | 朱文俊 周〓刚 王红斌 尹玉娟 张金江 郭创新 |
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| 作者单位： | 1. 广东电网公司电力科学研究院,广东广州,510080
2. 浙江大学电气工程学院,浙江杭州,310027 |
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| 基金项目： | 浙江省自然科学基金资助项目（Y1100243） |
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| 摘 要： | 针对支持向量机(SVM)用于变压器故障诊断中模型参数具有不确定性的问题,采用粒子群优化(PSO)算法对支持向量机参数进行优化,减少了模型参数的不确定性。故障数据测试表明,PSO能快速、准确地优化SVM参数,二者的结合可有效完成变压器故障分类,并取得较为满意的效果。
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| 关 键 词： | 油中溶解气体分析 PSO优化 支持向量机 故障诊断 参数优化 变压器 |
Fault Diagnosis of Transformers Based on PSO SVM |
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| ZHU Wenjun,ZHOU Gang,WANG Hongbin,YIN Yujuan,ZHANG Jinjiang and GUO Chuangxin.Fault Diagnosis of Transformers Based on PSO SVM[J].International Journal Hydroelectric Energy,2012,30(4):179-182. |
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| Authors: | ZHU Wenjun ZHOU Gang WANG Hongbin YIN Yujuan ZHANG Jinjiang and GUO Chuangxin |
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| Affiliation: | Electric Power Research Institute of Guangdong Power Grid Corporation, Guangzhou 510080, China;Electric Power Research Institute of Guangdong Power Grid Corporation, Guangzhou 510080, China;Electric Power Research Institute of Guangdong Power Grid Corporation, Guangzhou 510080, China;College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China |
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| Abstract: | To solve the parameters uncertainty of support vector machine (SVM) model when used in fault diagnosis of power transformers, particle swarm optimization (PSO) algorithm is adopted to optimize the parameters of SVM, which can decrease the uncertainty of model parameters. The results of fault example show that PSO can optimize the parameters of SVM rapidly and exactly; combination of PSO and SVM can be used in classification diagnosing faults of power transformers; it can obtain good effect. |
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| Keywords: | dissolved gas analysis PSO support vector machine fault diagnosis optimization parameters transformer |
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