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A Generalized Predictive Controller for a Wind Turbine Providing Frequency Support for a Microgrid

Author

Listed:
  • Carlos E. Prieto Cerón

    (Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Santo Andre 09210-580, Brazil)

  • Luís F. Normandia Lourenço

    (Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Santo Andre 09210-580, Brazil)

  • Juan S. Solís-Chaves

    (Mechatronic Enginering Department, ECCI University, Cl. 51 # 19-12, Bogotá 110231, Colombia)

  • Alfeu J. Sguarezi Filho

    (Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Santo Andre 09210-580, Brazil)

Abstract

The power system is moving away from the centralized generation paradigm. One of the current trends is the microgrid concept, where loads, small generators and renewable energy resources (RERs) that are in close proximity are controlled as one entity. Microgrids also allow for an increase in power availability as they can continue to supply electric power to loads even in the absence of a connection to the main grid. During the transition to islanded operation, microgrids may be subject to frequency disturbances caused by the power imbalance between load and generation. When microgrids contain high shares of renewable energy, the challenge is significantly higher due to the control strategies that aim to maximize power production, which are typically applied to RERs and render them insensitive to grid changes. Therefore, new control strategies need to be developed to enable the participation of RERs in the support of the frequency response. This work proposes a predictive control strategy that is based on a generalized predictive controller (GPC) being applied to the grid side converter of a doubly fed induction generator (DFIG) wind turbine to enable frequency support capabilities. The control objective was to track a time varying power reference signal that was generated according to the deviation from the nominal frequency, thereby enabling the energy storage device to inject power into the microgrid without a communication system. The GPC is a controller belonging to the family of model predictive controllers (MPCs), the main principles of which are the use of a system model to predict future states and the choice of an optimal input to ensure that the reference values are followed. To validate the proposed control strategy, a microgrid was simulated in MATLAB Simscape Electrical. The frequency response using the proposed GPC strategy was compared to another MPC-based strategy, known as finite control set, and a scenario in which the DFIG was not equipped with frequency support capabilities. The results show that the proposed strategy was able to improve the frequency response of the microgrid, reduce frequency oscillations and increase the value of the frequency nadir.

Suggested Citation

  • Carlos E. Prieto Cerón & Luís F. Normandia Lourenço & Juan S. Solís-Chaves & Alfeu J. Sguarezi Filho, 2022. "A Generalized Predictive Controller for a Wind Turbine Providing Frequency Support for a Microgrid," Energies, MDPI, vol. 15(7), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2562-:d:784897
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    References listed on IDEAS

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    1. Eltigani, Dalia & Masri, Syafrudin, 2015. "Challenges of integrating renewable energy sources to smart grids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 770-780.
    2. Luis. A. G. Gomez & Ahda P. Grilo & M. B. C. Salles & A. J. Sguarezi Filho, 2020. "Combined Control of DFIG-Based Wind Turbine and Battery Energy Storage System for Frequency Response in Microgrids," Energies, MDPI, vol. 13(4), pages 1-17, February.
    3. Mesbahi, Tedjani & Ouari, Ahmed & Ghennam, Tarak & Berkouk, El Madjid & Rizoug, Nassim & Mesbahi, Nadhir & Meradji, Moudrik, 2014. "A stand-alone wind power supply with a Li-ion battery energy storage system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 204-213.
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    Cited by:

    1. Raymundo Cordero & Thyago Estrabis & Gabriel Gentil & Matheus Caramalac & Walter Suemitsu & João Onofre & Moacyr Brito & Juliano dos Santos, 2022. "Tracking and Rejection of Biased Sinusoidal Signals Using Generalized Predictive Controller," Energies, MDPI, vol. 15(15), pages 1-13, August.
    2. Xianbo Du & Jilai Yu, 2022. "A Singular Spectrum Analysis and Gaussian Process Regression-Based Prediction Method for Wind Power Frequency Regulation Potential," Energies, MDPI, vol. 15(14), pages 1-16, July.
    3. Angelo Lunardi & Luís F. Normandia Lourenço & Enkhtsetseg Munkhchuluun & Lasantha Meegahapola & Alfeu J. Sguarezi Filho, 2022. "Grid-Connected Power Converters: An Overview of Control Strategies for Renewable Energy," Energies, MDPI, vol. 15(11), pages 1-33, June.

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