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Parameter Estimation of a Thermoelectric Generator by Using Salps Search Algorithm

Author

Listed:
  • Daniel Sanin-Villa

    (Departamento de Mecatrónica y Electromecánica, Instituto Tecnológico Metropolitano, Medellín 050036, Colombia)

  • Oscar Danilo Montoya

    (Grupo de Compatibilidad e Interferencia Electromagnética (GCEM), Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia)

  • Walter Gil-González

    (Department of Electrical Engineering, Faculty of Engineering, Universidad Tecnológica de Pereira, Pereira 660003, Colombia)

  • Luis Fernando Grisales-Noreña

    (Department of Electrical Engineering, Faculty of Engineering, Universidad de Talca, Curicó 3340000, Chile)

  • Alberto-Jesus Perea-Moreno

    (Departamento de Física Aplicada, Radiología y Medicina Física, Universidad de Córdoba, Campus de Rabanales, 14071 Córdoba, Spain)

Abstract

Thermoelectric generators (TEGs) have the potential to convert waste heat into electrical energy, making them attractive for energy harvesting applications. However, accurately estimating TEG parameters from industrial systems is a complex problem due to the mathematical complex non-linearities and numerous variables involved in the TEG modeling. This paper addresses this research gap by presenting a comparative evaluation of three optimization methods, Particle Swarm Optimization (PSO), Salps Search Algorithm (SSA), and Vortex Search Algorithm (VSA), for TEG parameter estimation. The proposed integrated approach is significant as it overcomes the limitations of existing methods and provides a more accurate and rapid estimation of TEG parameters. The performance of each optimization method is evaluated in terms of root mean square error (RMSE), standard deviation, and processing time. The results indicate that all three methods perform similarly, with average RMSE errors ranging from 0.0019 W to 0.0021 W, and minimum RMSE errors ranging from 0.0017 W to 0.0018 W. However, PSO has a higher standard deviation of the RMSE errors compared to the other two methods. In addition, we present the optimized parameters achieved through the proposed optimization methods, which serve as a reference for future research and enable the comparison of various optimization strategies. The disparities observed in the optimized outcomes underscore the intricacy of the issue and underscore the importance of the integrated approach suggested for precise TEG parameter estimation.

Suggested Citation

  • Daniel Sanin-Villa & Oscar Danilo Montoya & Walter Gil-González & Luis Fernando Grisales-Noreña & Alberto-Jesus Perea-Moreno, 2023. "Parameter Estimation of a Thermoelectric Generator by Using Salps Search Algorithm," Energies, MDPI, vol. 16(11), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4304-:d:1154785
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    References listed on IDEAS

    as
    1. Daniel Sanin-Villa, 2022. "Recent Developments in Thermoelectric Generation: A Review," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    2. Daniel Sanin-Villa & Oscar D. Monsalve-Cifuentes & Elkin E. Henao-Bravo, 2021. "Evaluation of Thermoelectric Generators under Mismatching Conditions," Energies, MDPI, vol. 14(23), pages 1-20, December.
    3. Andrés Alfonso Rosales-Muñoz & Jhon Montano & Luis Fernando Grisales-Noreña & Oscar Danilo Montoya & Fabio Andrade, 2022. "Optimal Power Dispatch of DGs in Radial and Mesh AC Grids: A Hybrid Solution Methodology between the Salps Swarm Algorithm and Successive Approximation Power Flow Method," Sustainability, MDPI, vol. 14(20), pages 1-32, October.
    4. Ju, Chengjian & Dui, Guansuo & Zheng, Helen Hao & Xin, Libiao, 2017. "Revisiting the temperature dependence in material properties and performance of thermoelectric materials," Energy, Elsevier, vol. 124(C), pages 249-257.
    5. Daniel Sanin-Villa & Oscar Danilo Montoya & Luis Fernando Grisales-Noreña, 2023. "Material Property Characterization and Parameter Estimation of Thermoelectric Generator by Using a Master–Slave Strategy Based on Metaheuristics Techniques," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
    6. Nimmanterdwong, Prathana & Chalermsinsuwan, Benjapon & Piumsomboon, Pornpote, 2023. "Optimizing utilization pathways for biomass to chemicals and energy by integrating emergy analysis and particle swarm optimization (PSO)," Renewable Energy, Elsevier, vol. 202(C), pages 1448-1459.
    7. Boukouvala, Fani & Misener, Ruth & Floudas, Christodoulos A., 2016. "Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO," European Journal of Operational Research, Elsevier, vol. 252(3), pages 701-727.
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    Cited by:

    1. Daniel Sanin-Villa & Miguel Angel Rodriguez-Cabal & Luis Fernando Grisales-Noreña & Mario Ramirez-Neria & Juan C. Tejada, 2024. "A Comparative Analysis of Metaheuristic Algorithms for Enhanced Parameter Estimation on Inverted Pendulum System Dynamics," Mathematics, MDPI, vol. 12(11), pages 1-17, May.

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