支持向量机及在电力系统中的应用  

Study on Support Vector Machines Algorithms and Its Application to Power Systems

作　　者：胡国胜[1] 

机构地区：[1]安庆师范学院计算机与信息学院

出　　处：《高电压技术》2007年第4期101-105,共5页High Voltage Engineering

基　　金：国家自然科学基金(50077008);广东省自然科学基金(033044)~~

摘　　要：支持向量机是20世纪60年代开始研究,在90年代得到快速发展的机器学习技术。为了系统地归纳统计学习理论与支持向量机的基本思想和算法,总结目前该领域的最新研究成果。通过对7种多分类支持向量机训练算法进行深入分析,得出其各算法的优、缺点,还归纳了支持向量机在故障预测和识别、电力系统等方面的应用,特别在电力系统暂态稳定评估与分析、电机故障诊断、高压输电线路故障诊断和定位、双凸极永磁发电机非线性模型、火焰监测以及电力系统负荷预测等方面的成功应用。研究表明,支持向量机克服了传统神经网络算法的局部最优、收敛难以控制、结构设计困难等优点。The basic statistical learning theory （SLT） and its corresponding algorithms, support vector machines （SVMs）, are surveyed, and especially, its latest research results are summarized and studied. By deeply analyzing seven main multi-classification training algorithms of SVMs, including the one-against-rest algorithm, one-againstone algorithm, hierarchy classification, k-class classification, QP-MC-SV algorithm, DDAGSVM algorithm, and sphere structure classification, their respective merits and demerits are found out and listed. Finally, this paper concludes many applications of SVM in the area of fault identification and power systems, specially in the area of power systems transient stability analysis （TSA） and evaluation, fault diagnostics of an electrical machine, accurate fault location in the power transmission line, nonlinear model building of fielding-winding doubly salient generator, flame monitoring, and power system short-term load forecasting and so on. Results demonstrate that SVMs overcome inherent shortcomings of traditional neural network, such as not global optimization, convergence not easily controlling , and difficult network structure designing.

关 键 词：统计学习理论 支持向量机 多分类算法 电力系统 故障诊断 负荷预测 

分 类 号：TM711[电气工程—电力系统及自动化]