采用自适应广义S变换与概率神经网络的电能质量扰动识别

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摘要针对S变换窗函数固定,时-频分辨率有限的缺点,提出一种根据电能质量扰动信号傅里叶谱特征,自适应调整窗函数宽度的广义S变换用于分析扰动信号。首先,根据暂态扰动信号傅里叶谱,确定信号主要频率成分,由此初步确定扰动类型。然后,根据扰动信号对时间与频率分辨率的不同需求,自适应调整广义S变换的窗宽调整因子,控制窗口宽度。将优化后的自适应广义S变换用于分析扰动信号。最后,提取7种特征,构造概率神经网络分类器,识别含2种复合扰动在内的10种扰动信号。仿真实验证明,新方法分类过程简单,识别准确率高,抗噪声干扰能力强。

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	黄南天
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关键词 ：电能质量扰动, 扰动识别, 广义S变换, 自适应, 概率神经网络

Abstract：This paper presents a new generalized S-transform with a self-adaptive window width factor based on the Fourier spectrum of disturbances signals. The adaptive generalized S-transform (AGST) has better time-frequency resolution for disturbances recognition than traditional S-transform. Firstly, the main frequency component is calculated from the Fast Fourier transform spectrum of disturbance signals. The types of disturbances are rough separated into subsets. Secondly, the value of window width factor is adaptive changed with the requirement of time and frequency resolution for disturbances recognition. Finally, seven features are extracted from the results of AGST and the new classifier based on probabilistic neural networks is constructed for recognizing ten types of disturbances include two types of complex disturbances. The simulation experiments show that the simple new method has high classification accuracy and anti-noise ability.

Key words： keywords：Power quality disturbances disturbances recognition generalized S-transform adaptive probabilistic neural networks

收稿日期: 2012-09-20 出版日期: 2013-12-12

PACS:

TM714.3

基金资助:国家自然科学基金(51237002、51277042),吉林省教育厅“十二五”科学技术研究(吉教科合字[2011]第262号)资助项目。

作者简介: 黄南天男，1980年生，博士，讲师，主要研究方向为电能质量控制及人工智能、模式识别技术在电力系统中的应用等。徐殿国男，1960年生，博士，教授，博士生导师，研究方向为电能质量控制、电力电子技术、照明电子学等。

引用本文:

黄南天, 徐殿国, 蔡国伟. 采用自适应广义S变换与概率神经网络的电能质量扰动识别[J]. 电工技术学报, 2013, 28(1增): 209-216. Huang Nantian, Xu Dianguo, Liu Xiaosheng. Identification of Power Quality Disturbances Based on Adaptive Generalized S-Transform and Probabilistic Neural Networks. Transactions of China Electrotechnical Society, 2013, 28(1增): 209-216.

链接本文:

http://dgjsxb.ces-transaction.com/CN/Y2013/V28/I1增/209

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