location: Current position: Home >> Scientific Research >> Paper Publications

Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network

Hits:

Indexed by:期刊论文

First Author:Cai, Kewei

Correspondence Author:Cao, WP (reprint author), Aston Univ, Sch Engn & Appl Sci, Birmingham B4 7ET, W Midlands, England.

Co-author:Alalibo, Belema Prince,Cao, Wenping,Liu, Zheng,Wang, Zhiqiang,Li, Guofeng

Date of Publication:2018-11-01

Journal:ENERGIES

Included Journals:SCIE

Volume:11

Issue:11

ISSN No.:1996-1073

Key Words:deep stochastic configuration network (DSCN); harmonics analysis; power quality (PQ) disturbance; power system; variational mode decomposition (VMD)

Abstract:This paper proposes a novel, two-stage and hybrid approach based on variational mode decomposition (VMD) and the deep stochastic configuration network (DSCN) for power quality (PQ) disturbances detection and classification in power systems. Firstly, a VMD technique is applied to discriminate between stationary and non-stationary PQ events. Secondly, the key parameters of VMD are determined as per different types of disturbance. Three statistical features (mean, variance, and kurtosis) are extracted from the instantaneous amplitude (IA) of the decomposed modes. The DSCN model is then developed to classify PQ disturbances based on these features. The proposed approach is validated by analytical results and actual measurements. Moreover, it is also compared with existing methods including wavelet network, fuzzy and S-transform (ST), adaptive linear neuron (ADALINE) and feedforward neural network (FFNN). Test results have proved that the proposed method is capable of providing necessary and accurate information for PQ disturbances in order to plan PQ remedy actions accordingly.

Pre One:高压脉冲放电破碎菱镁矿石的实验研究

Next One:介质阻挡放电脱色酸性橙II 废水的动力学及脱色物化效应