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


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.

Publication DOI: https://doi.org/10.3390/en11113040
Divisions: Engineering & Applied Sciences > Electrical, Electronic & Power Engineering
Engineering & Applied Sciences
Additional Information: © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Uncontrolled Keywords: deep stochastic configuration network (DSCN),power quality (PQ) disturbance,harmonics analysis,power system,variational mode decomposition (VMD)
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Related URLs: https://www.mdp ... 1073/11/11/3040 (Publisher URL)
PURE Output Type: Article
Published Date: 2018-11-05
Accepted Date: 2018-10-31
Authors: Cai, Kewei
Alalibo, Belema
Cao, Wenping (ORCID Profile 0000-0002-8133-3020)
Liu, Zheng
Wang, Zhiqiang
Li, Guofeng



Version: Published Version

License: Creative Commons Attribution

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