基于优化DDAGSVM多类分类策略的电能质量扰动识别  

Power quality disturbance recognition based on improved DDAGSVM multi-class classification strategy

作　　者：任子晖 王琦 

机构地区：[1]中国矿业大学信息与控制工程学院,江苏徐州221008

出　　处：《电力系统保护与控制》2018年第5期82-88,共7页Power System Protection and Control

基　　金：江苏省重点研发计划项目(BE2016046);江苏省煤矿电气与自动化工程实验室建设项目(2014KJZX05)

摘　　要：针对电能质量扰动类型多样且识别率不高的问题,该研究的目的是如何将多类分类问题应用于支持向量机。首先通过S变换和FFT变换提取扰动信号特征量进行模型训练。其次将广义KKT判定条件与样本空间分布序列相结合引入类间识别度,将类间识别度最高的超平面函数作为分类器根节点,以此克服传统决策导向非循环图支持向量机分类器(DDAGSVM)在分类生成顺序上随机化的缺点,并将改进的DDAGSVM应用于电能扰动信号的识别分类。实验结果表明,所提算法较传统DDAGSVM算法有良好效果和更好的鲁棒性。In order to solve the problem that the power quality disturbance is diverse and the recognition rate is not high, the purpose of this paper is how to apply the multi-class classification problem to the support vector machine. Firstly, the disturbance signal eigenvalue is extracted to train model by S transform and FFT transform. Secondly, the generalized KKT decision condition is combined with the sample space distribution sequence to introduce interclass recognition degree. The hyperplane function with the highest interclass degree is used as the root node of the classifier to overcome the shortcomings of traditional Decision-oriented Non-cyclic Graph Support Vector Machine Classifier （DDAGSVM） randomization in the order of classification generation, and the improved DDAGSVM is applied to the classification of the energy disturbance signal. The experimental results show that the proposed algorithm has better effect and better robustness than the traditional DDAGSVM algorithm.

关 键 词：支持向量机 决策导向非循环图 类间识别度 广义KKT条件 空间分布序列 

分 类 号：TM711[电气工程—电力系统及自动化]