Parameter Estimation of a Thermoelectric Generator by Using Salps Search Algorithm

Daniel Sanin-Villa, Oscar Danilo Montoya, Walter Gil-González, Luis Fernando Grisales-Noreña () and Alberto-Jesus Perea-Moreno ()
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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

Energies, 2023, vol. 16, issue 11, 1-16

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.

Keywords: thermoelectric generators; master–slave strategy; root mean square error standard deviation; standard deviation analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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