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

Kewei Cai,Guofeng Li,Wenping Cao,Belema Alalibo,Zheng Liu,Zhiqiang Wang

doi:10.3390/en11113040

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.

Highlights

In power systems, power quality (PQ) has been a significant issue that is disrupted by increasing uncertain, intermittent, renewable energy penetration on the generation side [1,2] and increasing uptake of electric vehicles (EVs) on the demand side [3,4,5]
PQ refers to multifarious electromagnetic phenomena that deviate voltage and current from ideal waveforms, which are known as PQ disturbances (PQD)
Results demonstrated that the method based on the variational mode decomposition (VMD) and deep stochastic configuration network (DSCN) can achieve an excellent detection and classification rates for proposed method based on the VMD and DSCN can achieve an excellent detection and classification rates for disturbances

Summary

Introduction

Power quality (PQ) has been a significant issue that is disrupted by increasing uncertain, intermittent, renewable energy penetration on the generation side [1,2] and increasing uptake of electric vehicles (EVs) on the demand side [3,4,5]. PQ refers to multifarious electromagnetic phenomena that deviate voltage and current from ideal waveforms, which are known as PQ disturbances (PQD). The presence of PQD can be divided into sags, swells, interruptions, oscillations, flickers, harmonics (interharmonics), notches, spikes, and their combinations, as per the international standards such as IEEE-1159, IEC 61000, and EN 50160 [6,7,8]. These disturbances greatly affect the safe and economical operations of power systems, decreasing the lifetime and performance of electrical equipment connected to the system.

Results

Discussion

Conclusion