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Frequency Regulation System: A Deep Learning Identification, Type-3 Fuzzy Control and LMI Stability Analysis

Author

Listed:
  • Ayman A. Aly

    (Department of Mechanical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Bassem F. Felemban

    (Department of Mechanical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Ardashir Mohammadzadeh

    (Independent Researcher, 1148 Baku, Azerbaijan)

  • Oscar Castillo

    (Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22414, Mexico)

  • Andrzej Bartoszewicz

    (Institute of Automatic Control, Lodz University of Technology, 18 Stefanowskiego St., 90-537 Łódź, Poland)

Abstract

In this paper, the problem of frequency regulation in the multi-area power systems with demand response, energy storage system (ESS) and renewable energy generators is studied. Dissimilarly to most studies in this field, the dynamics of all units in all areas are considered to be unknown. Furthermore time-varying solar radiation, wind speed dynamics, multiple load changes, demand response (DR), and ESS are considered. A novel dynamic fractional-order model based on restricted Boltzmann machine (RBM) and deep learning contrastive divergence (CD) algorithm is presented for online identification. The controller is designed by the dynamic estimated model, error feedback controller and interval type-3 fuzzy logic compensator (IT3-FLC). The gains of error feedback controller and tuning rules of the estimated dynamic model are extracted through the fractional-order stability analysis by the linear matrix inequality (LMI) approach. The superiority of a schemed controller in contrast to the type-1 and type-2 FLCs is demonstrated in various conditions, such as time-varying wind speed, solar radiation, multiple load changes, and perturbed dynamics.

Suggested Citation

  • Ayman A. Aly & Bassem F. Felemban & Ardashir Mohammadzadeh & Oscar Castillo & Andrzej Bartoszewicz, 2021. "Frequency Regulation System: A Deep Learning Identification, Type-3 Fuzzy Control and LMI Stability Analysis," Energies, MDPI, vol. 14(22), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7801-:d:684747
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    References listed on IDEAS

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    Cited by:

    1. Qiuzhen Wang & Jiangping Hu, 2023. "Modeling and Control of Wide-Area Networks," Mathematics, MDPI, vol. 11(18), pages 1-24, September.
    2. Cinthia Peraza & Patricia Ochoa & Oscar Castillo & Zong Woo Geem, 2022. "Interval-Type 3 Fuzzy Differential Evolution for Designing an Interval-Type 3 Fuzzy Controller of a Unicycle Mobile Robot," Mathematics, MDPI, vol. 10(19), pages 1-17, September.

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