Power Quality Disturbance Recognition Base on Maximum Variance Unfolding

Authors

Linlin Che, College of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China;
Yinghui Kong, College of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China;
Zhixiong Chen, College of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China;

Abstract

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.

Recommended Citation

Che, Linlin; Kong, Yinghui; and Chen, Zhixiong (2019) "Power Quality Disturbance Recognition Base on Maximum Variance Unfolding," Journal of System Simulation: Vol. 31: Iss. 8, Article 24.
DOI: 10.16182/j.issn1004731x.joss.17-0295
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss8/24

First Page

1702

Revised Date

2017-09-18

DOI Link

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

Last Page

1710

CLC

TM711

Recommended Citation

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.

DOI

10.16182/j.issn1004731x.joss.17-0295