利用快速S变换的电能质量扰动识别方法

满蔚仕 1; 张志禹 1; 康青 2; 苗永康 1; 席晓莉 1

doi:10.7652/xjtuxb201308023

您当前的位置：

首页 >

文章列表页 >

利用快速S变换的电能质量扰动识别方法

更新时间：2025-05-08

- 利用快速S变换的电能质量扰动识别方法
- Fast S Transform-Based Classification of Power Quality Disturbance
- 西安交通大学学报 2013年47卷第8期页码：133-140
- 作者机构：
  
  西安理工大学自动化学院,西安,710048
- 作者简介：
- 基金信息：
  
  : 2012-12-28。作者简介: 满蔚仕(1969—),男,讲师。基金项目: 国家自然科学基金资助项目(61271091);陕西省教育厅科学研究计划资助项目(2013JK09937)
- DOI：10.7652/xjtuxb201308023
  中图分类号： TM711
- 网络首发：2013-08-10，
  
  纸质出版：2013
- 稿件说明：
移动端阅览
满蔚仕 1, 张志禹 1, 康青 2, 等. 利用快速S变换的电能质量扰动识别方法[J]. 西安交通大学学报, 2013,47(8):133-140.

Fast S Transform-Based Classification of Power Quality Disturbance[J]. 2013, 47(8): 133-140.
满蔚仕 1, 张志禹 1, 康青 2, 等. 利用快速S变换的电能质量扰动识别方法[J]. 西安交通大学学报, 2013,47(8):133-140. DOI： 10.7652/xjtuxb201308023.

Fast S Transform-Based Classification of Power Quality Disturbance[J]. 2013, 47(8): 133-140. DOI： 10.7652/xjtuxb201308023.

摘要

针对当前基于S变换的电能质量方法计算开销大、不能实时识别电能质量扰动的问题

提出利用快速S变换与最小二乘支持向量机相结合的识别电能质量干扰新方法。该方法从快速S变换得到的一维向量中提取各频率段模系数的标准偏差、最大模系数及额定频率对应的模系数作为特征向量

利用最小二乘支持向量机对电压骤升、电压骤降、电压中断、暂态脉冲、暂态振荡、谐波等几种电能质量干扰进行分类和识别。研究结果表明:与传统的基于S变换的电能质量方法相比

该方法在2个方面节省了时间

一是减少了提取特征量所用的时间

二是由于特征向量数据较少

采用支持向量机样本训练时间减少; 特别是当电压扰动信号持续时间越长时节省效率越高

在同样准确性下

对于长度为1 024点的扰动信号

节省了约99%的时间; 除此之外

该方法对信号分类的正确率可达98%

同时还具有较高的抗干扰能力。

Abstract

Focusing on higher computation cost and lack of real-time detection for all techniques based on traditional S-transform to identify power quality disturbances

a real-time approach combining fast S-transform with least squares support vector machine is proposed. The standard deviation of module coefficients

maximum module coefficient of each frequency band

and module coefficient corresponding to the rated frequency are extracted from the one-dimensional vector of the fast S-transform of the original power quality signals as features

and the least squares support vector machine based on optimized parameters and the minimum output coding is used to classify and identify the voltage swell

voltage sag

voltage interruption

spike

transient oscillation and harmonic waves. Compared with the traditional approach based on S-transform

the proposed approach reduces the tasks in both extracting features and training of the support vector machine classifier due to fewer training samples. The longer the duration of the voltage disturbance signal

the higher the saving efficiency. To the same accuracy

for the disturbance signal with a length of 1 024 points

processing time can be saved by 99%. The classification accuracy of this approach gets up to 98% with higher anti-interference ability.

关键词

Keywords

references

占勇,程浩忠,丁屹峰,等. 基于S变换的电能质量扰动支持向量机分类识别 [J]. 中国电机工程学报, 2005,25(4):51-56.

ZHAN Yong, CHENG Haozhong, DING Yifeng, et al. S-transform-based classification of power quality disturbance signals by support vector machines [J]. Proceedings of the CSEE, 2005,24(4):51-56.

赵凤展,杨仁刚. 基于短时傅里叶变换的电压暂降扰动检测 [J]. 中国电机工程学报, 2007,27(10):28-34.

ZHAO Fengzhan, YANG Rengang. Voltage sag disturbance detection based on short time Fourier transform [J]. Proceedings of the CSEE, 2007,27(10):28-34.

李霖,杨洪耕. 基于二维离散平稳小波的电能质量扰动分类 [J]. 电力系统自动化, 2007,31(10):21-26.

LI Lin,YANG Honggeng. Short duration power quality disturbance signal classification method based on two-dimensional discrete static wavelet transform [J]. Automation of Electric Power Systems, 2007,31(10):21-26.

何为,杨洪耕. 基于第二代小波变换和离散隐马尔可夫模型的电能质量扰动分类 [J]. 电工技术学报, 2007,22(5):146-152.

HE Wei, YANG Honggeng. Disturbance classification based on second generation wavelet transform and discrete hidden Markov models [J]. Transactions of China Electrotechnical Society, 2007,22(5):146-152.

王成山,王继东. 基于小波包分解的电能质量扰动分类方法 [J]. 电网技术, 2004,28(15):78-82.

WANG Chengshan, WANG Jidong. Classification method of power quality disturbance based on wavelet packet decomposition [J]. Power System Technology, 2004,28(15):78-82.

秦英林,田立军,常学飞. 基于小波变换能量分布和神经网络的电能质量扰动分类 [J]. 电力系统自动化设备, 2009,29(7):64-66.

QIN Yinglin, TIAN Lijun, CHANG Xuefei. Classification of power quality disturbance based on wavelet energy distribution and neural network [J]. Electric Power Automation Equipment, 2009,29(7):64-66.

SANTOSO S, POWERS E J, GRADY W M. Power quality disturbance identification using wavelet transforms and artificial neural networks [C]∥Proceedings of IEEE ICHQP Ⅶ. Piscataway,NJ,USA:IEEE, 1996:615-618.

刘守亮,肖先勇,杨洪耕. 基于S变换模时频矩阵相似度的短时电能质量扰动分类 [J]. 电网技术, 2006,30(5):67-71.

LIU Shouliang, XIAO Xianyong, YANG Honggeng. Classification of short duration power quality disturbance based on module time-frequency matrixes similarity by S-transform [J]. Power System Technology, 2006,30(5):67-71.

XIAO Xianyong, XU Fangwei, YANG Honggeng. Short duration disturbance classifying based on S-transform maximum similarity [J]. International Journal of Electrical Power and Energy Systems, 2009,31(7):374-378.

黄南天,徐殿国,刘晓胜. 基于 S 变换与 SVM 的电能质量复合扰动识别 [J]. 电工技术学报, 2011,26(10):23-30.

HUANG Nantian, XU Dianguo, LIU Xiaosheng. Identification of power quality complex disturbances based on S-transform and SVM [J]. Transactions of China Electrotechnical Society, 2011,26(10):23-30.

赵凤展,杨仁刚. 基于S 变换和时域分析的电能质量扰动识别 [J]. 电网技术, 2006,30(15):90-94.

ZHAO Fengzhan, YANG Rengang. Power quality disturbances classification based on S-transform and time domain analysis [J]. Power System Technology, 2006,30(15):90-94.

杨洪耕,刘守亮,肖先勇,等. 基于S变换的电压凹陷分类专家系统 [J]. 中国电机工程学报, 2007,27(1):98-104.

YANG Honggeng, LIU Shouliang, XIAO Xianyong, et al. S-transform-based expert system for classification of voltage dips [J]. Proceedings of the CSEE, 2007,27(1):98-104.

沈跃,刘国海,刘慧. 基于改进S变换和贝叶斯相关向量机的电能质量扰动识别 [J]. 控制与决策, 2011,26(4):587-591.

SHEN Yue, LIU Guohai, LIU Hui. Classification identification of power quality disturbances based on modified S-transform and Bayes relevance vector machine [J]. Control and Decision, 2011,26(4):587-591.

BROWN R A, LAUZON L M, FRAYNE R. A general description of linear time-frequency transforms and formulation of a fast,invertible transform that samples the continuous S-transform spectrum nonredundantly [J]. IEEE Trans on Signal Processing, 2010,58(1):281-290.

张全明,刘会金. 最小二乘支持向量机在电能质量扰动分类中的应用 [J]. 中国电机工程学报, 2008,28(1):106-110.

ZHANG Quanming, LIU Huijin. Application of LS-SVM in classification of power quality disturbances [J]. Proceedings of the CSEE, 2008,28(1):106-110.

杨延西,刘丁. 基于小波变换和最小二乘支持向量机的短期电力负荷预测 [J]. 电网技术, 2005,29(13):60-64.

YANG Yanxi, LIU Ding. Short-term load forecasting based on wavelet transform and least square support vector machine [J]. Power System Technology, 2005,29(13):60-64.

易吉良,彭建春,罗安,等. 电能质量信号的改进S变换降噪方法 [J]. 仪器仪表学报, 2010,31(1):32-37.

YI Jiliang, PENG Jianchun, LUO An, et al. Power quality signal denoising using modified S-transform [J]. Chinese Journal of Scientific Instrument, 2010,31(1):32-37.

高静怀,满蔚仕,陈树民. 广义S 变换域有色噪声与信号识别方法 [J]. 地球物理学报, 2004,22(5):869-875.

GAO Jinghuai, MAN Weishi, CHEN Shumin. Recognition of signal from colored noise background in generalized S-transform domain [J]. Chinese Journal of Geophysics, 2004,22(5):869-875.

PINNEGAR C R, MANSIHA L. Time-local spectral analysis for non-stationary time series: the S-transform for noise signal [J]. Fluctuation and Noise Letters, 2003,3(3):357-364.

MAN Weishi, GAO Jinghuai. Statistical denoising of signals in the S-transform domain [J]. Computers Geosciences, 2009,35(6):1079-1086.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

面向分类去噪问题的模糊支持向量机新算法

一种新的浮选泡沫图像识别方法

泛化的统一切比雪夫多项式核函数

基于样本熵的注意力相关脑电特征信息提取与分类

一种新的选择性支持向量机集成学习算法