基于ANP-SVM算法的智能变电站过程层网络故障分类
|
摘要
针对现有的智能变电站过程层网络故障分类所面临的效率低下、数据集噪音等问题,提出一种基于ANP-SVM的过程层网络故障分类算法。该算法首先利用改进的分离间隔法对SVM进行核参数以及误差参数的优化选取,然后将经过抗噪声处理的样本数据输入优化后的SVM,从而使得分类更加精确、高效。实验结果表明,本算法在过程层网络故障分类中具有较好的性能。
Aiming at the inefficiency and data set noise of the existing process layer network fault classification in intelligent substation, this paper proposes an ANP-SVM based process layer network fault classification algorithm. Firstly, the improved separation interval method is used to optimize the selection of kernel parameters and error parameters of SVM, and then the anti-noise sample data is input into the optimized SVM, which makes the classification more accurate and efficient. The experimental results show that the algorithm has good performance in the process layer network fault classification.
Aiming at the inefficiency and data set noise of the existing process layer network fault classification in intelligent substation, this paper proposes an ANP-SVM based process layer network fault classification algorithm. Firstly, the improved separation interval method is used to optimize the selection of kernel parameters and error parameters of SVM, and then the anti-noise sample data is input into the optimized SVM, which makes the classification more accurate and efficient. The experimental results show that the algorithm has good performance in the process layer network fault classification.
引文
[1] 邱智勇,陈建民,朱炳铨.基于IEC 61850标准的500 kV三层结构数字化变电站建设[J].电力系统自动化,2009,33(12):103-107.
[2] IEC 61850-8-1:2004,Communication Networks and Systems in Substations–Part 8-1:Specific Communication Service Mapping (SCSM)–Mappings to MMS (ISO 9506-1 and ISO 9506-2) and to ISO/IEC 8802-3[S].
[3] 马峻,赵飞乐,徐潇,等.MRA-PCA-PSO组合优化BP神经网络模拟电路故障诊断研究[J].电子测量与仪器学报,2018,32(3):73-79.
[4] 雍明超,吕侠,周钟,等.基于朴素贝叶斯算法的电力变压器故障诊断方法研究[J].电气应用,2017,36(14):32-35.
[5] XIAO Y C,WANG H G,XU W L.Parameter selection of Gaussian kernel for one-class SVM[J].IEEE Transactions on Cybernetics,2017,45(5):941-953.
[6] 曹万鹏,罗云彬,史辉.一种支持向量机算法设计中优化的混合加权核函数选取与样本加权方法[J].小型微型计算机系统,2018,39(2):340-346.
[7] ZHOU M,WANG B,LI T T,et al.Multi-objective unit commitment under hybrid uncertainties:A data-driven approach[C]// Proceedings of the 2018 IEEE 15th International Conference on Networking,Sensing and Control (ICNSC).2018,DOI:10.1109/ICNSC.2018.8361330.
[8] 朱碧钦,吴飞,罗富财.基于大数据的全业务统一数据中心数据分析域建设研究[J].电力信息与通信技术,2017,15(2):91-96.
[9] 舒醒,于慧敏,郑伟伟,等.基于边际Fisher准则和迁移学习的小样本集分类器设计算法[J].自动化学报,2016,42(9):1313-1321.
[10] LIU Y F,PI D C,CHENG Q Y.Ensemble kernel method:SVM classification based on game theory[J].Journal of Systems Engineering and Electronics,2016,27(1):251-259.
[11] JING C,HOU J.SVM and PCA based fault classification approaches for complicated industrial process[J].Neurocomputing,2015,167:636-642.
[12] 张智胜,张云鹏,刘青.支持向量机和小波包分析下的轴承故障诊断[J].机械设计与制造,2017(3):204-207.
[13] 张勇,李丹,文福拴,等.基于IEC 61850的智能变电站过程层故障诊断[J].电力建设,2018,39(3):42-48.
[14] 李诒靖,郭海湘,李亚楠,等.一种基于Boosting的集成学习算法在不均衡数据中的分类[J].系统工程理论与实践,2016,36(1):189-199.
[15] FRANTI P,YANG J W.Medoid-shift for noise removal to improve clustering[C]// Proceedings of the 2018 International Conference on Artificial Intelligence and Soft Computing.2018:604-614.
[16] CHERKASSKY V,MA Y Q.Practical selection of SVM parameters and noise estimation for SVM regression[J].Neural Networks,2004,17(1):113-126.
[17] VARATHARAJAN R,MANOGARAN G,PRIYAN M K.A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing[J].Multimedia Tools and Applications,2018,77(8):10195-10215.
[18] 安悦瑄,丁世飞,胡继普.孪生支持向量机综述[J].计算机科学,2018,45(11):29-36.
[19] 许洪华,李勇,施恂山,等.基于振动噪声SC与SVM的变压器局部过热故障诊断[J].陕西电力,2017,45(6):11-17.
[20] 孙毅,李世豪,崔灿,等.基于高斯核函数改进的电力用户用电数据离群点检测方法[J].电网技术,2018,42(5):1595-1604.
[21] 甘锡淞,李云,傅成华,等.基于信息融合和CS-SVM的变压器绕组变形故障诊断方法研究[J].电力系统保护与控制,2018,46(1):156-161.
[22] 张君琦,杨帆,郭谋发.配电网高阻接地故障时频特征SVM分类识别方法[J].电气技术,2018,19(3):37-43.
[23] 王德成,林辉.一种SVM不平衡分类方法及在故障诊断的应用[J].电机与控制学报,2012,16(9):48-52.
[2] IEC 61850-8-1:2004,Communication Networks and Systems in Substations–Part 8-1:Specific Communication Service Mapping (SCSM)–Mappings to MMS (ISO 9506-1 and ISO 9506-2) and to ISO/IEC 8802-3[S].
[3] 马峻,赵飞乐,徐潇,等.MRA-PCA-PSO组合优化BP神经网络模拟电路故障诊断研究[J].电子测量与仪器学报,2018,32(3):73-79.
[4] 雍明超,吕侠,周钟,等.基于朴素贝叶斯算法的电力变压器故障诊断方法研究[J].电气应用,2017,36(14):32-35.
[5] XIAO Y C,WANG H G,XU W L.Parameter selection of Gaussian kernel for one-class SVM[J].IEEE Transactions on Cybernetics,2017,45(5):941-953.
[6] 曹万鹏,罗云彬,史辉.一种支持向量机算法设计中优化的混合加权核函数选取与样本加权方法[J].小型微型计算机系统,2018,39(2):340-346.
[7] ZHOU M,WANG B,LI T T,et al.Multi-objective unit commitment under hybrid uncertainties:A data-driven approach[C]// Proceedings of the 2018 IEEE 15th International Conference on Networking,Sensing and Control (ICNSC).2018,DOI:10.1109/ICNSC.2018.8361330.
[8] 朱碧钦,吴飞,罗富财.基于大数据的全业务统一数据中心数据分析域建设研究[J].电力信息与通信技术,2017,15(2):91-96.
[9] 舒醒,于慧敏,郑伟伟,等.基于边际Fisher准则和迁移学习的小样本集分类器设计算法[J].自动化学报,2016,42(9):1313-1321.
[10] LIU Y F,PI D C,CHENG Q Y.Ensemble kernel method:SVM classification based on game theory[J].Journal of Systems Engineering and Electronics,2016,27(1):251-259.
[11] JING C,HOU J.SVM and PCA based fault classification approaches for complicated industrial process[J].Neurocomputing,2015,167:636-642.
[12] 张智胜,张云鹏,刘青.支持向量机和小波包分析下的轴承故障诊断[J].机械设计与制造,2017(3):204-207.
[13] 张勇,李丹,文福拴,等.基于IEC 61850的智能变电站过程层故障诊断[J].电力建设,2018,39(3):42-48.
[14] 李诒靖,郭海湘,李亚楠,等.一种基于Boosting的集成学习算法在不均衡数据中的分类[J].系统工程理论与实践,2016,36(1):189-199.
[15] FRANTI P,YANG J W.Medoid-shift for noise removal to improve clustering[C]// Proceedings of the 2018 International Conference on Artificial Intelligence and Soft Computing.2018:604-614.
[16] CHERKASSKY V,MA Y Q.Practical selection of SVM parameters and noise estimation for SVM regression[J].Neural Networks,2004,17(1):113-126.
[17] VARATHARAJAN R,MANOGARAN G,PRIYAN M K.A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing[J].Multimedia Tools and Applications,2018,77(8):10195-10215.
[18] 安悦瑄,丁世飞,胡继普.孪生支持向量机综述[J].计算机科学,2018,45(11):29-36.
[19] 许洪华,李勇,施恂山,等.基于振动噪声SC与SVM的变压器局部过热故障诊断[J].陕西电力,2017,45(6):11-17.
[20] 孙毅,李世豪,崔灿,等.基于高斯核函数改进的电力用户用电数据离群点检测方法[J].电网技术,2018,42(5):1595-1604.
[21] 甘锡淞,李云,傅成华,等.基于信息融合和CS-SVM的变压器绕组变形故障诊断方法研究[J].电力系统保护与控制,2018,46(1):156-161.
[22] 张君琦,杨帆,郭谋发.配电网高阻接地故障时频特征SVM分类识别方法[J].电气技术,2018,19(3):37-43.
[23] 王德成,林辉.一种SVM不平衡分类方法及在故障诊断的应用[J].电机与控制学报,2012,16(9):48-52.