基于LMD能量熵和GK模糊聚类的电能质量扰动识别

doi:10.3969/j.issn.1000-1158.2016.01.21

Abstract
Figure/Table
References (13)
Related Citation (15)

Download: PDF (1761 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract A new approach for power quality disturbance identification based on local mean decomposition (LMD) energy entropy and GK fuzzy clustering is introduced. LMD energy entropy has localized features and can represent the complexity of disturbance signals, quantizing disturbance characteristic. GK fuzzy clustering can process data with irregular distribution, so as to identify various disturbance signals. Non-stationary disturbance signals are decomposed by LMD, obtaining a number of product function (PF) components with physical meaning, which is screened out by Shannon entropy feature selection methods to obtain the energy entropies and construct the eigenvectors. The constructed eigenvectors are further put into GK classifier to recognize different identification types. The experiment demonstrated that the method is able to identify disturbance signals accurately, with better anti-noise performance.

Key words： metrology power quality disturbance signal local mean decomposition energy entropy GK fuzzy clustering

Received: 20 August 2014 Published: 10 December 2015

PACS:

TB971

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	ZHANG Shu-qing
	LI Pan
	FENG Lu
	LI Nan
	ZHANG Hang-fei
	QIAO Yong-jing
	XU Jian-tao

Cite this article:

ZHANG Shu-qing,LI Pan,FENG Lu, et al. Power Quality Disturbance Identification Based on LMD Energy Entropy and GK Fuzzy Clustering[J]. Acta Metrologica Sinica, 2016, 37(1): 90-95.

URL:

http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2016.01.21 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2016/V37/I1/90