基于改进鱼群算法和支持向量机的变压器故障诊断
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摘要
电力变压器在整个体系中处于十分重要的地位,部件的运行概况和整个电网的稳定性具有密切联系。对电力变压器的故障诊断,工程实践中广泛采用的是油中溶解气体法,由于变压器故障样本比较少,属于小样本数据,而支持向量机能够较好地解决小样本的多分类问题,因此提出利用改进鱼群算法对支持向量机寻优得到全局最优解,得到具有最佳参数的支持向量机模型。通过数据实例分析得出,改进鱼群算法故障诊断模型比粒子群算法故障诊断模型和改良三比值法分类准确率高。
Power transformer holds a very important position in the whole system,for its operational status is closely related to the stability of the whole power grid. Oil dissolved gas method is widely applied in engineering practice for fault diagnosis of the power transformer.Since transformer fault samples with a small sample database are quite limited in number and the support vector machine( SVM) is a good solution of the multi-class problem of small samples,this paper proposes that the improved fish swarm algorithm should be used for SVM optimization to obtain a global optimal solution and a SVM model of optimal parameters. Analysis of data instances shows the fault diagnosis model of improved fish swarm algorithm can achieve a higher accuracy than the particle swarm optimization fault diagnosis method and the improved three-ratio approach.
Power transformer holds a very important position in the whole system,for its operational status is closely related to the stability of the whole power grid. Oil dissolved gas method is widely applied in engineering practice for fault diagnosis of the power transformer.Since transformer fault samples with a small sample database are quite limited in number and the support vector machine( SVM) is a good solution of the multi-class problem of small samples,this paper proposes that the improved fish swarm algorithm should be used for SVM optimization to obtain a global optimal solution and a SVM model of optimal parameters. Analysis of data instances shows the fault diagnosis model of improved fish swarm algorithm can achieve a higher accuracy than the particle swarm optimization fault diagnosis method and the improved three-ratio approach.
引文
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[11]苟博,黄贤武.支持向量机多类分类方法[J].数据采集与处理,2006,21(3):334-339.
[12]王有元,陈壁君.基于层次分析结构的变压器健康状态与综合寿命评估模型[J].电网技术,2014,38(10):2845-2850.
[2]董明,严璋,杨莉,等.基于证据推理的电力变压器故障诊断策略[J].中国电机工程学报,2006,26(1):107-114.
[3]陈伟根,潘翀,云玉新,等.基于小波网络及油中溶解气体分析的电力变压器故障诊断方法[J].中国电机工程学报,2008,28(7):121-126.
[4]禹建丽,卞帅.基于BP神经网络的变压器故障诊断模型[J].系统仿真学报,2014,26(6):1343-1349.
[5]谢可夫,罗安.遗传算法在变压器故障诊断中的应用[J].电力自动化设备,2005,25(4):55-58.
[6]孙才新,郭俊峰,廖瑞金,等.变压器油中溶解气体分析中的模糊模式多层聚类故障诊断方法的研究[J].中国电机工程学报,2001,21(2):37-41.
[7]费胜巍,孙宇.融合粗糙集与灰色理论的电力变压器故障预测[J].中国电机工学报,2008,28(16):154-160.
[8]VAPNIK V N.The nature of statistical learning theory[M].New York:Springer-Verlag,1995.
[9]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002,22(11):32-38.
[10]毕建权,鹿鸣明,郭创新,等.一种基于多分类概率输出的变压器故障诊断方法[J].电力系统自动化,2015,39(5):88-100.
[11]苟博,黄贤武.支持向量机多类分类方法[J].数据采集与处理,2006,21(3):334-339.
[12]王有元,陈壁君.基于层次分析结构的变压器健康状态与综合寿命评估模型[J].电网技术,2014,38(10):2845-2850.