| 引用本文: | 黄南天,张卫辉,徐殿国,蔡国伟,刘闯,张书鑫.采用多分辨率广义S变换的电能质量扰动识别[J].哈尔滨工业大学学报,2015,47(9):51.DOI:10.11918/j.issn.0367-6234.2015.09.010 |
| HUANG Nantian,ZHANG Weihui,XU Dianguo,CAI Guowei,LIU Chuang,ZHANG Shuxin.Classification of power quality disturbances utilizing multiresolution generalized S-transform[J].Journal of Harbin Institute of Technology,2015,47(9):51.DOI:10.11918/j.issn.0367-6234.2015.09.010 |
|
| 摘要: |
| 为提高电能质量复合扰动识别能力,提出一种采用多分辨率广义S变换（multiresolution generalized S-transform, GST）的扰动识别方法. 首先,将信号频谱分为低频、中频、高频3个频域,分别设定窗宽调整因子,使其在各个频域具有不同的时-频分辨率,满足不同扰动信号识别要求. 并针对高频振荡识别问题,设计基于基频傅里叶谱特征的自适应窗宽调整方法. 在此基础上,提取6种特征用于构建决策树. 最后,提出最小分类损失原则,确定决策树节点分类阈值,设计扰动分类器. 仿真与实测信号实验证明,新方法能够准确识别含5种复合扰动在内的13种扰动. 相较于S变换、广义S变换和Hyperbolic S变换,新方法具有更好的特征表现能力,分类效果好,抗噪声干扰能力强. |
| 关键词: 电能质量 电能质量暂态扰动 S变换 多分辨率 决策树 |
| DOI：10.11918/j.issn.0367-6234.2015.09.010 |
| 分类号:TM714.3 |
| 基金项目:国家自然科学基金 (51307020);吉林省科技发展计划(20150520114JH);吉林市科技发展计划(201464052). |
|
| Classification of power quality disturbances utilizing multiresolution generalized S-transform |
|
HUANG Nantian1, ZHANG Weihui1, XU Dianguo2, CAI Guowei1, LIU Chuang1, ZHANG Shuxin1
|
|
(1. School of Electrical Engineering, Northeast Dianli University, 132012 Jilin, Jilin,China; 2. School of Electrical Engineering and Automation, Harbin Institute of Technology, 150001 Harbin, China)
|
| Abstract: |
| In order to improve the ability of complex power quality disturbances recognition, a new type of complex disturbances recognition approach based on Multiresolution Generalized S-transform (MGST) is proposed. Firstly, the spectrum of original signals is segmented into 3 frequency areas including low frequency area, medium frequency area and high frequency area. The width factor of window function in S-transform is defined respectively in different frequency areas. MGST has different time-frequency resolution in each frequency area in order to satisfy the recognition requirements of different disturbances in each frequency area. Otherwise, the width factor of window function in the high frequency area is adaptively adjusted according to the value of Fourier spectrum of the fundamental frequency. On this basis, the decision tree based on 6 features is constructed to recognize disturbance signals. Finally, the minimum classification faults rule is designed to get the optimum threshold of each node. The simulation and real signals experiments show that 13 types of disturbances including 5 types of complex disturbances are recognized accurately by the new approach. The new approach has better classification accuracy and noise immunity than other methods such as S-transform , generalized S-transform and Hyperbolic S-transform. |
| Key words: power quality power quality disturbance S-transform multiresolution decision tree |
