| 引用本文: | 田凤兰,张恩泽,潘思蓉,等.基于特征量优选与ICA-SVM的变压器故障诊断模型[J].电力系统保护与控制,2019,47(17):163-170. |
| TIAN Fenglan,ZHANG Enze,PAN Sirong,et al.Fault diagnosis model of power transformers based on feature quantity optimization and ICA-SVM[J].Power System Protection and Control,2019,47(17):163-170 |
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| 摘要: |
| 为了弥补现有变压器故障诊断方法在油中气体分析(DGA)特征量选取和诊断模型方面的不足，采用IEC三比值法中的3种气体比值作为变压器故障诊断的特征量。同时从含有8种油中溶解气体中任意3种及以上的共254种气体组合中筛选出准确率最高的3组最优DGA特征气体组合，将其作为对照组特征量。然后采用帝国竞争算法(ICA)优化支持向量机的变压器故障诊断模型(ICA-SVM)，与标准支持向量机(SVM)法、粒子群优化向量机(PSO-SVM)以及IEC三比值法进行对比。实例结果表明:三气体比值特征量相比3组最优DGA气体组合，故障识别准确率提高了10%左右；ICA-SVM故障诊断模型相比标准SVM法、PSO-SVM和IEC三比值法故障识别准确率提高了7%~35%；综合三比值特征量与ICA-SVM故障诊断模型的准确率为89.3%，相较其他几种方法准确率提升了7%~35%。结果验证了该方法的有效性和准确性。 |
| 关键词: 电力变压器 故障诊断 三比值 帝国竞争算法 支持向量机 |
| DOI：10.19783/j.cnki.pspc.181259 |
| 分类号: |
| 基金项目:国家自然科学基金项目资助(51867003);广西自然科学基金资项目资助(2015GXNSFBA139235);广西科技厅项目资助(AE020069);国网河南省电力公司科技项目资助(52170215000V) |
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| Fault diagnosis model of power transformers based on feature quantity optimization and ICA-SVM |
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TIAN Fenglan1,,2,ZHANG Enze2,PAN Sirong2,HANG Ying2,REN Guangwei2,FAN Xianhao2,ZHANG Heng2
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(1. Electric Power Research Institute of State Grid Henan Electric Power Company, Zhengzhou 450052, China;2. Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China)
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| Abstract: |
| In order to make up for the shortcomings of the existing transformer fault diagnosis methods in the Dissolved Gas Analysis (DGA) quantity feature selection and diagnostic model,this paper proposes to use three gas ratios in the IEC three-ratio method as the characteristic quantity of transformer fault diagnosis. Three groups of optimal DGA characteristic gases with the highest accuracy are selected from 254 gas combinations containing any three or more dissolved gases in eight kinds of oils, which is taken as the characteristic quantity of the control group. Then, the Empire Competing Algorithm (ICA) optimized support vector machine transformer fault diagnosis model (ICA-SVM) is compared with standard Support Vector Machine (SVM) method, Particle Swarm Optimization Vector Machine (PSO-SVM) and IEC three-ratio method. The results show that the accuracy of fault identification is improved by about 10% compared with the optimal DGA gas combination of three groups; the accuracy of ICA-SVM fault diagnosis model increased by 7% to 35% compared with the standard SVM method, PSO-SVM and IEC method; the accuracy rate of the integrated three-ratio feature and the ICA-SVM fault diagnosis model is 89.3%, which is 7% ~ 35% higher than that of the other methods. Therefore, the validity and accuracy of the proposed method are verified. This work is supported by National Natural Science Foundation of China (No. 51867003), Guangxi Natural Science Foundation (No. 2015GXNSFBA139235), Guangxi Science and Technology Department Project (No. AE020069), and Science and Technology Project of State Grid Henan Electric Power Company (No. 52170215000V). |
| Key words: power transformer fault diagnosis triple ratio imperial competition algorithm SVM |
