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

Kewei Cai, Belema Prince Alalibo, Wenping Cao, Zheng Liu, Zhiqiang Wang and Guofeng Li
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Kewei Cai: College of Information Engineering, Dalian Ocean University, Dalian 116023, China
Belema Prince Alalibo: School of Engineering and Applied Science, Aston University, Birmingham, B4 7ET, UK
Wenping Cao: School of Engineering and Applied Science, Aston University, Birmingham, B4 7ET, UK
Zheng Liu: School of Electrical Engineering, Dalian University of Technology, Dalian 116023, China
Zhiqiang Wang: School of Electrical Engineering, Dalian University of Technology, Dalian 116023, China
Guofeng Li: School of Electrical Engineering, Dalian University of Technology, Dalian 116023, China

Energies, 2018, vol. 11, issue 11, 1-18

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.

Keywords: deep stochastic configuration network (DSCN); harmonics analysis, power quality (PQ) disturbance; power system; variational mode decomposition (VMD) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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