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Hybrid Controller Based on LQR Applied to Interleaved Boost Converter and Microgrids under Power Quality Events

Author

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  • Gerardo Humberto Valencia-Rivera

    (School of Engineering and Sciences, Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, Nuevo León, Mexico)

  • Ivan Amaya

    (School of Engineering and Sciences, Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, Nuevo León, Mexico)

  • Jorge M. Cruz-Duarte

    (School of Engineering and Sciences, Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, Nuevo León, Mexico)

  • José Carlos Ortíz-Bayliss

    (School of Engineering and Sciences, Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, Nuevo León, 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, Guanajuato, Mexico)

Abstract

Renewable energy sources are an environmentally attractive idea, but they require a proper control scheme to guarantee optimal operation. In this work, we tune different controllers for an Interleaved Boost Converter (IBC) powered by a photovoltaic array using three metaheuristics: Genetic Algorithm, Particle Swarm Optimization, and Gray Wolf Optimization. We also develop several controllers for a second simulated scenario where the IBC is plugged into an existing microgrid (MG) as this can provide relevant data for real-life applications. In both cases, we consider hybrid controllers based on a Linear Quadratic Regulator (LQR). However, we hybridize it with an Integral action (I-LQR) in the first scenario to compare our data against previously published controllers. In the second one, we add a Proportional-Integral technique (PI-LQR) as we do not have previous data to compare against to provide a more robust controller than I-LQR. To validate our approach, we run extensive simulations with each metaheuristic and compare the resulting data. We focus on two fronts: the performance of the controllers and the computing cost of the solvers when facing practical issues. Our results demonstrate that the approach proposed for tuning controllers is a feasible strategy. The controllers tuned with the metaheuristics outperformed previously proposed strategies, yielding solutions thrice faster with virtually no overshoot and a voltage ripple seven times smaller. Not only this, but our controllers could correct some issues liaised to the IBC when it is plugged into an MG. We are confident that these insights can help migrate this approach to a more diverse set of MGs with different renewable sources and escalate it to real-life experiments.

Suggested Citation

  • Gerardo Humberto Valencia-Rivera & Ivan Amaya & Jorge M. Cruz-Duarte & José Carlos Ortíz-Bayliss & Juan Gabriel Avina-Cervantes, 2021. "Hybrid Controller Based on LQR Applied to Interleaved Boost Converter and Microgrids under Power Quality Events," Energies, MDPI, vol. 14(21), pages 1-31, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:6909-:d:661368
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    References listed on IDEAS

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    Cited by:

    1. Rongchao Niu & Hongyu Zhang & Jian Song, 2023. "Model Predictive Control of DC–DC Boost Converter Based on Generalized Proportional Integral Observer," Energies, MDPI, vol. 16(3), pages 1-16, January.

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