基于广义S变换与PSO-PNN的电能质量扰动识别  

Power quality disturbances classification based on generalized S-transform and PSO-PNN

作　　者：覃星福 龚仁喜[1] 

机构地区：[1]广西大学电气工程学院,广西南宁530004

出　　处：《电力系统保护与控制》2016年第15期10-17,共8页Power System Protection and Control

基　　金：国家自然科学基金项目(61561007)~~

摘　　要：为了克服从电网电能质量监测系统的大数据中自动识别出电能质量扰动的困难,提出了一种基于广义S变换与PSO-PNN的电能质量扰动识别新方法。该方法利用了广义S变换能兼顾时频分辨率的特点,首先使用广义S变换分析扰动信号的时频特性,接着从广义S变换模矩阵中提取出扰动信号的时频特征量,然后用PSO-PNN分类器对扰动信号进行分类识别。PSO算法的使用克服了PNN的平滑因子没有确定选取方法的缺陷,使分类器性能大大提升。仿真实验结果表明,该方法能够对常见的6种电能质量扰动进行高效的分类识别,分类正确率高,对噪声不敏感,具有良好的应用价值。To overcome the difficulty of automatic identification of power quality disturbances from the large data of power quality monitoring system, a new method for power quality disturbances identification is proposed based on generalized S-transform and PSO-PNN. It makes full use of generalized S-transform＇s ability of giving attention to both time and frequency resolution. Initially, the time-frequency analysis of power quality disturbances is carried out by using generalized S-transform, from whose results the time-frequency features of disturbances are extracted. Finally, PSO-PNN, as a classifier, is used to identify power quality disturbances. The PSO algorithm solves the problem of choosing the smoothing factor for PNN which is usually hard to determine, and thus the performance of the classifier is greatly improved. The simulation results show that the proposed method can identify six kinds of power quality disturbances correctly and effectively, and it is characterized by high recognition correctness rate and low sensitivity to noises, and it will find extensive application.

关 键 词：电力系统 电能质量 广义S变换 PSO-PNN 分类识别 

分 类 号：TM711[电气工程—电力系统及自动化]