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Open AccessArticle

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

1
College of Information Engineering, Dalian Ocean University, Dalian 116023, China
2
School of Engineering and Applied Science, Aston University, Birmingham, B4 7ET, UK
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School of Electrical Engineering, Dalian University of Technology, Dalian 116023, China
*
Author to whom correspondence should be addressed.
Energies 2018, 11(11), 3040; https://doi.org/10.3390/en11113040
Received: 28 September 2018 / Revised: 19 October 2018 / Accepted: 31 October 2018 / Published: 5 November 2018
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. View Full-Text
Keywords: deep stochastic configuration network (DSCN); harmonics analysis, power quality (PQ) disturbance; power system; variational mode decomposition (VMD) deep stochastic configuration network (DSCN); harmonics analysis, power quality (PQ) disturbance; power system; variational mode decomposition (VMD)
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Cai, K.; Alalibo, B.P.; Cao, W.; Liu, Z.; Wang, Z.; Li, G. Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network. Energies 2018, 11, 3040.

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