基于多核支持向量机的混合扰动波形辨识算法研究  

Complex disturbance waveform recognition based on a multi-kernel support vector machine

作　　者：张明龙 张振宇 罗翔 高源 李宽宏 朱珂[3] ZHANG Minglong;ZHANG Zhenyu;LUO Xiang;GAO Yuan;LI Kuanhong;ZHU Ke(Electrical Power Research Institute,State Grid Fujian Electric Power Co.,Ltd.,Fuzhou 350007,China;Fuzhou Power Supply Company,State Grid Fujian Electric Power Co.,Ltd.,Fuzhou 350009,China;School of Electrical Engineering,Shandong University,Jinan 250061,China)

机构地区：[1]国网福建省电力有限公司电力科学研究院,福建福州350007 [2]国网福建省电力有限公司福州供电公司,福建福州350009 [3]山东大学电气工程学院,山东济南250061

出　　处：《电力系统保护与控制》2022年第15期43-49,共7页Power System Protection and Control

基　　金：国家电网有限公司总部科技项目资助“基于物联网技术的配电开关一二次深度融合与精益运维关键技术研究及应用”(52130421000S)。

摘　　要：针对特征提取手段自身局限性导致的扰动典型特征间边缘重叠对混和扰动辨识的影响,提出一种基于多域特征优选的多核支持向量机辨识算法。首先,利用多种特征提取手段获取混和扰动多域典型特征。其次,为考虑高维特征与目标类别的相关性和度量尺度的规范化,利用改进的最大相关最小冗余准则优选用于辨识的关键特征子集,进而利用计及半径信息的多核SVM来辨识混合扰动波形。仿真结果表明,所提辨识算法能够克服混合扰动特征空间模糊对辨识精度的影响,受噪声影响小,稳定性好。There is influence of edge overlap among disturbed typical features on complex disturbance identification due to the limitations of feature extraction methods.Thus a multi-kernel support vector machine identification algorithm based on multi-domain feature optimization is proposed.First,a variety of feature extraction methods are used to obtain the complex perturbation multi-domain typical features.Secondly,in order to consider the correlation between high-dimensional features and target categories and the normalization of the measurement scale,an improved maximum correlation minimum redundancy criterion is used to select the key feature subset for identification,and then the multi-kernel SVM with radius information is used to identify the complex disturbance waveform.The simulation results show that the proposed algorithm can overcome the influence of spatial ambiguity of complex disturbance on identification accuracy,is less affected by noise and has good stability.

关 键 词：混合扰动 多域 多核支持向量机 边缘重叠 配电网 

分 类 号：TM711[电气工程—电力系统及自动化] TP181[自动化与计算机技术—控制理论与控制工程]