摘要
为了提高故障诊断的准确率,提出了一种多分类最小二乘支持向量机(LS-SVM)和改进粒子群优化(PSO)相结合的电力变压器故障诊断方法。引入最小输出编码构造多个2分类LS-SVM,实现了变压器诊断的多类分类。利用PSO算法获得LS-SVM诊断模型的最优参数,并采用交叉验证原理来提高分类算法的整体泛化性能。实例分析结果表明,采用LS-SVM和PSO算法可以准确、有效地对变压器进行故障诊断;与传统的电力变压器故障诊断方法相比,该方法的诊断准确率更高。
We proposed a fault diagnosis method based on the multi-class least squares support vector machine（LS-SVM） and the improved particle swarm optimization（PSO） algorithm to improve the accuracy of transformer fault diagnosis. By introducing the minimum output coding, we built several two-class LS-SVMs to realize the multi-class classification of transformer diagnosis. Then we obtained the optimal parameters of LS-SVM diagnosis model using the PSO algorithm, and improved the generalization performance of this multi-class algorithm through the cross validation. Study of practical cases indicate that, after using the PSO and LS-SVM algorithm, transformer faults can be diagnosed effectively and accurately, and the accuracy is higher than that of a number of conventional transformer fault diagnosis approaches.
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2014年第11期3424-3429,共6页
High Voltage Engineering
关键词
最小二乘支持向量机
多类分类
粒子群优化
故障诊断
电力变压器
准确率
least squares support vector machine
multi-class classification
particle swarm optimization
fault diagnosis
power transformers
accuracies
