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

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  • Kewei Cai

    (College of Information Engineering, Dalian Ocean University, Dalian 116023, China
    School of Engineering and Applied Science, Aston University, Birmingham, B4 7ET, UK)

  • 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)

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.

Suggested Citation

  • Kewei Cai & Belema Prince Alalibo & Wenping Cao & Zheng Liu & Zhiqiang Wang & Guofeng Li, 2018. "Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network," Energies, MDPI, vol. 11(11), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3040-:d:180745
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    References listed on IDEAS

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    1. Juan-José González-de-la-Rosa & Agustín Agüera-Pérez & José-Carlos Palomares-Salas & Olivia Florencias-Oliveros & José-María Sierra-Fernández, 2018. "A Dual Monitoring Technique to Detect Power Quality Transients Based on the Fourth-Order Spectrogram," Energies, MDPI, vol. 11(3), pages 1-12, February.
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    5. Huaishuo Xiao & Jianchun Wei & Qingquan Li, 2017. "Identification of Combined Power Quality Disturbances Using Singular Value Decomposition (SVD) and Total Least Squares-Estimation of Signal Parameters via Rotational Invariance Techniques (TLS-ESPRIT)," Energies, MDPI, vol. 10(11), pages 1-16, November.
    6. Changhong Deng & Yahong Chen & Jin Tan & Pei Xia & Ning Liang & Weiwei Yao & Yuan-ao Zhang, 2017. "Distributed Variable Droop Curve Control Strategies in Smart Microgrid," Energies, MDPI, vol. 11(1), pages 1-17, December.
    7. Huihui Wang & Ping Wang & Tao Liu, 2017. "Power Quality Disturbance Classification Using the S-Transform and Probabilistic Neural Network," Energies, MDPI, vol. 10(1), pages 1-19, January.
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    Cited by:

    1. Rodrigo De A. Teixeira & Werbet L. A. Silva & Guilherme A. P. De C. A. Pessoa & Joao T. Carvalho Neto & Elmer R. L. Villarreal & Andrés O. Salazar & Alberto S. Lock, 2020. "One Cycle Control of a PWM Rectifier a New Approach," Energies, MDPI, vol. 13(20), pages 1-23, October.
    2. Jun Deng & Jun Suo & Jing Yang & Shutao Peng & Fangde Chi & Tong Wang, 2019. "Adaptive Damping Control Strategy of Wind Integrated Power System," Energies, MDPI, vol. 12(1), pages 1-18, January.

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