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Hybrid LQR-PI Control for Microgrids under Unbalanced Linear and Nonlinear Loads

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  • Gerardo Humberto Valencia-Rivera

    (Telematics (CA), Engineering Division (DICIS), Campus Irapuato-Salamanca, University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Mexico)

  • Luis Ramon Merchan-Villalba

    (Telematics (CA), Engineering Division (DICIS), Campus Irapuato-Salamanca, University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Mexico)

  • Guillermo Tapia-Tinoco

    (Telematics (CA), Engineering Division (DICIS), Campus Irapuato-Salamanca, University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Mexico)

  • Jose Merced Lozano-Garcia

    (Telematics (CA), Engineering Division (DICIS), Campus Irapuato-Salamanca, University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Mexico)

  • Mario Alberto Ibarra-Manzano

    (Telematics (CA), Engineering Division (DICIS), Campus Irapuato-Salamanca, University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Mexico)

  • Juan Gabriel Avina-Cervantes

    (Telematics (CA), Engineering Division (DICIS), Campus Irapuato-Salamanca, University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, Salamanca 36885, Mexico
    Author thanks the Universidad de Guanajuato by the financial support of the APC.)

Abstract

A hybrid Linear Quadratic Regulator (LQR) and Proportional-Integral (PI) control for a MicroGrid (MG) under unbalanced linear and nonlinear loads was presented and evaluated in this paper. The designed control strategy incorporates the microgrid behavior, low-cost LQR, and error reduction in the stationary state by the PI control, to reduce the overall energetic cost of the classical PI control applied to MGs. A Genetic Algorithm (GA) calculates the parameters of LQR with high-accuracy fitness function to obtain the optimal controller parameters as settling time and overshoot. The gain values of the classical PI controller were determined through the improved LQR values and geometrical root locus. When MG operates in the grid-tied mode under unbalanced conditions, the controller performance of the Current Source Inverter (CSI) of the MG is considerably affected. Consequently, the CSI operates in a negative-sequence mode to compensate for unbalanced current at the Point of Common Coupling (PCC) between the MG and the utility grid. The study cases involved the reduction of the negative-sequence percentage in the current at the PCC, mitigation of harmonics in the current signal injected by the MG, and close related power quality issues. All these cases have been analyzed by implementing an MG connected at the PCC of a low-voltage distribution network. A numerical model of the MG in Matlab/Simulink was implemented to verify the performance of the designed LQR-PI control to mitigate or overcome the power quality concerns. The extensive simulations have permitted verifying the robustness and effectiveness of the proposed strategy.

Suggested Citation

  • Gerardo Humberto Valencia-Rivera & Luis Ramon Merchan-Villalba & Guillermo Tapia-Tinoco & Jose Merced Lozano-Garcia & Mario Alberto Ibarra-Manzano & Juan Gabriel Avina-Cervantes, 2020. "Hybrid LQR-PI Control for Microgrids under Unbalanced Linear and Nonlinear Loads," Mathematics, MDPI, vol. 8(7), pages 1-25, July.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:7:p:1096-:d:380315
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    References listed on IDEAS

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    1. Hirsch, Adam & Parag, Yael & Guerrero, Josep, 2018. "Microgrids: A review of technologies, key drivers, and outstanding issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 402-411.
    2. Seyyed Yousef Mousazadeh Mousavi & Alireza Jalilian & Mehdi Savaghebi & Josep M. Guerrero, 2017. "Flexible Compensation of Voltage and Current Unbalance and Harmonics in Microgrids," Energies, MDPI, vol. 10(10), pages 1-19, October.
    3. S. Augusti Lindiya & N. Subashini & K. Vijayarekha, 2019. "Cross Regulation Reduced Optimal Multivariable Controller Design for Single Inductor DC-DC Converters," Energies, MDPI, vol. 12(3), pages 1-26, February.
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    Cited by:

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    2. Marcel Nicola & Claudiu-Ionel Nicola, 2022. "Improvement of Linear and Nonlinear Control for PMSM Using Computational Intelligence and Reinforcement Learning," Mathematics, MDPI, vol. 10(24), pages 1-34, December.

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