基于最大方差展开法的电能质量扰动识别

doi:10.16182/j.issn1004731x.joss.17-0295

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (8): 1702-1710.doi: 10.16182/j.issn1004731x.joss.17-0295

基于最大方差展开法的电能质量扰动识别

车辚辚, 孔英会, 陈智雄

华北电力大学电气与电子工程学院,河北保定 071003

收稿日期:2017-06-28 修回日期:2017-09-18 发布日期:2019-12-12
作者简介:车辚辚(1981-),女,河北保定,硕士,高工,研究方向为电能质量分析等。
基金资助:
国家自然科学基金(61771195, 61601182),中央高校科研业务费资助(2015MS100),河北省自然科学基金 (F2017502059)

Power Quality Disturbance Recognition Base on Maximum Variance Unfolding

Che Linlin, Kong Yinghui, Chen Zhixiong

College of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China

Received:2017-06-28 Revised:2017-09-18 Published:2019-12-12

摘要/Abstract

摘要： 针对电能质量扰动(power quality disturbance,PQD)的复杂性,提出了一种基于最大方差展开(maximum variance unfolding,MVU)非线性流行学习的PQD特征提取方法,结合分类器算法完成了对PQD识别。对PQD信号进行小波分解得到信号的小波能量作为原始特征集;通过MVU算法对原始特征集进行压缩,由于在算法中引入核函数将非凸二次规划转化为凸半正定最优化问题,从而得到信息量更集中且很好保持训练数据分布边界的低维PQD特征;结合分类器算法完成PQD识别。实验结果表明,MVU算法约简后得到PQD特征向量,不仅有效降低了特征向量个数,而且对PQD的识别准确率高,有一定的工程应用前景。

关键词: 电能质量扰动, 流行学习, 最大方差展开, 特征提取, 扰动识别

Abstract: In view of the complexity of power quality disturbance (PQD), a recognition method of PQD feature extraction method based on maximum variance unfolding (MVU) is proposed and PQD recognition is completed by combining classifier algorithm in this paper. The wavelet energy features of PQD are extracted by wavelet transform to construct the original feature set.For the reason that non-convex quadratic programming is transformed into convex semi-definite optimization problem by kernel function, the MVU method is applied to compress the sample features into 3-dimension features which keep low dimensional structure in high dimensional space and make a rough recognition of PQD. General classifiers are used to verify the method. The experimental results show that this MVU method reduces the number of eigenvectors and improves the recognition accuracy of the PQD. It is promising in engineering.

Key words: power quality disturbance, manifold learning, maximum variance unfolding, features extraction, disturbance recognition

中图分类号:

TM711

车辚辚, 孔英会, 陈智雄. 基于最大方差展开法的电能质量扰动识别[J]. 系统仿真学报, 2019, 31(8): 1702-1710.

Che Linlin, Kong Yinghui, Chen Zhixiong. Power Quality Disturbance Recognition Base on Maximum Variance Unfolding[J]. Journal of System Simulation, 2019, 31(8): 1702-1710.

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基于最大方差展开法的电能质量扰动识别

Power Quality Disturbance Recognition Base on Maximum Variance Unfolding

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