基于S变换和径向基神经网络的暂态电能质量扰动识别
李新华
【期刊名称】《自动化应用》 【年(卷),期】2011(000)009
【摘要】This paper presents a method of identification of transient power quality disturbance based on S transform and RBF neural network. This method, first uses S transform to preprocess transient power quality disturbance waveform and extract 5 related feature capacity by use of statistical methods, then classifies the characteristics of the sample volume by RBF neural network .Simulation results show that the program＇s high accuracy, strong anti-noise ability, few training samples and rapid response.%提出一种基于S变换和径向基神经网络的暂态电能质量扰动识别的方法。该方法首先用S变换对暂态电能质量扰动波形进行预处理．使用统计的方法提取了5个相关特征量，然后用径向基神经网络对提取的特征量样本进行分类。仿真结果表明，该方案正确率高，抗噪声能力强，训练样本少，响应快速． 【总页数】5页(50-53,66)
【关键词】电能质量;S变换;径向基神经网络;电能质量扰动分类 【作者】李新华
【作者单位】湖南省电力公司益阳安化电力局,湖南安化413500 【正文语种】中文 【中图分类】TM71
基于S变换和径向基神经网络的暂态电能质量扰动识别
