基于GWO-SVM的电压暂降扰动源识别

针对电压暂降扰动事件发生频繁、扰动种类多样,难以有效识别扰动源的实际情况,结合电压暂降扰动信号的时-频特性、灰狼优化算法(GWO)和支持向量机(SVM)分类模型,提出了一种电压暂降扰动源识别新方法。通过S变换对电压暂降扰动信号进行多分辨率时-频分析,从S变换结果矩阵中提取出信号的特征曲线,建立6类电压暂降混合扰动信号的8个特征量。构建GWO-SVM一对余(OVR)分类器,以提取出的特征量作为输入,对扰动源进行分类识别。基于MATLAB/Simulink构建电压暂降模型,经仿真验证分析,该方法可以有效识别电压暂降扰动源,也为电压暂降扰动治理提供必要的技术支撑。

Identification of voltage sag disturbance sources based on GWO-SVM

In view of the actual situation that voltage sags occur frequently with diverse categories, which makes it difficult to identify the disturbance sources, a novel identification approach of voltage sag disturbance sources was proposed by combing the time-frequency characteristic of voltage sag disturbance signals, the grey wolf optimization (GWO) and support vector machine(SVM) in this paper .Multiresolution time-frequency analysis was applied to voltage sag disturbance signals by S transform, extracting the feature curves of signals from S transform result matrix, and then 8 features were calculated from 6 kinds of voltage sag complex disturbance signals. A one versus rest (OVR) GWO-SVM classifier whose inputs were fed with the extracted features was established to identify voltage sag disturbance sources. The proposed method was validated to be effective to identify the voltage sag disturbance sources by the analysis result of voltage sag simulation model based on MATLAB/Simulink, which could be also a necessary technical support for voltage sag disturbance governance.