Parameter Estimation of a Thermoelectric Generator by Using Salps Search Algorithm
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...
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MDPI AG
2023-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/11/4304 |
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| author | Daniel Sanin-Villa Oscar Danilo Montoya Walter Gil-González Luis Fernando Grisales-Noreña Alberto-Jesus Perea-Moreno |
| author_facet | Daniel Sanin-Villa Oscar Danilo Montoya Walter Gil-González Luis Fernando Grisales-Noreña Alberto-Jesus Perea-Moreno |
| author_sort | Daniel Sanin-Villa |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T03:08:53Z |
| format | Article |
| id | doaj.art-b7e81b17e92b46b396edd8b6e5b0cc68 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T03:08:53Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-b7e81b17e92b46b396edd8b6e5b0cc682023-11-18T07:47:01ZengMDPI AGEnergies1996-10732023-05-011611430410.3390/en16114304Parameter Estimation of a Thermoelectric Generator by Using Salps Search AlgorithmDaniel Sanin-Villa0Oscar Danilo Montoya1Walter Gil-González2Luis Fernando Grisales-Noreña3Alberto-Jesus Perea-Moreno4Departamento de Mecatrónica y Electromecánica, Instituto Tecnológico Metropolitano, Medellín 050036, ColombiaGrupo de Compatibilidad e Interferencia Electromagnética (GCEM), Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, ColombiaDepartment of Electrical Engineering, Faculty of Engineering, Universidad Tecnológica de Pereira, Pereira 660003, ColombiaDepartment of Electrical Engineering, Faculty of Engineering, Universidad de Talca, Curicó 3340000, ChileDepartamento de Física Aplicada, Radiología y Medicina Física, Universidad de Córdoba, Campus de Rabanales, 14071 Córdoba, SpainThermoelectric 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.https://www.mdpi.com/1996-1073/16/11/4304thermoelectric generatorsmaster–slave strategyroot mean square error standard deviationstandard deviation analysis |
| spellingShingle | Daniel Sanin-Villa Oscar Danilo Montoya Walter Gil-González Luis Fernando Grisales-Noreña Alberto-Jesus Perea-Moreno Parameter Estimation of a Thermoelectric Generator by Using Salps Search Algorithm Energies thermoelectric generators master–slave strategy root mean square error standard deviation standard deviation analysis |
| title | Parameter Estimation of a Thermoelectric Generator by Using Salps Search Algorithm |
| title_full | Parameter Estimation of a Thermoelectric Generator by Using Salps Search Algorithm |
| title_fullStr | Parameter Estimation of a Thermoelectric Generator by Using Salps Search Algorithm |
| title_full_unstemmed | Parameter Estimation of a Thermoelectric Generator by Using Salps Search Algorithm |
| title_short | Parameter Estimation of a Thermoelectric Generator by Using Salps Search Algorithm |
| title_sort | parameter estimation of a thermoelectric generator by using salps search algorithm |
| topic | thermoelectric generators master–slave strategy root mean square error standard deviation standard deviation analysis |
| url | https://www.mdpi.com/1996-1073/16/11/4304 |
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