Regresión Lineal para la Identificación del Punto de Máxima Potencia en Microrredes Híbridas Implementado en HYPERSIM

Carlos Lozada; David Panchi; Wilson Sánchez; Andrés Jacho

doi:10.37116/revistaenergia.v20.n2.2024.618

Linear Regression for the Identification of the Maximum Power Point in Hybrid Microgrids Implemented in HYPERSIM

Regresión Lineal para la Identificación del Punto de Máxima Potencia en Microrredes Híbridas Implementado en HYPERSIM

Published 2024-01-17

https://doi.org/10.37116/revistaenergia.v20.n2.2024.618

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Vol. 20 No. 2 (2024): Revista Técnica "energía", Edición No. 20, ISSUE II

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SISTEMAS ELÉCTRICOS DE POTENCIA

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Linear Regression for the Identification of the Maximum Power Point in Hybrid Microgrids Implemented in HYPERSIM. (2024). Revista Técnica "energía", 20(2), PP. 34-46. https://doi.org/10.37116/revistaenergia.v20.n2.2024.618

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https://doi.org/10.37116/revistaenergia.v20.n2.2024.618

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Linear Regression for the Identification of the Maximum Power Point in Hybrid Microgrids Implemented in HYPERSIM. (2024). Revista Técnica "energía", 20(2), PP. 34-46. https://doi.org/10.37116/revistaenergia.v20.n2.2024.618

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Carlos Lozada

Operador Nacional de Electricidad, CENACE

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David Panchi

Celec EP, Unidad de Negocio Coca Codo Sinclair

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Wilson Sánchez

Operador Nacional de Electricidad, CENACE

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https://orcid.org/0009-0009-1537-4850

Andrés Jacho

Operador Nacional de Electricidad, CENACE

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https://orcid.org/0009-0004-0170-6010

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MPPT

Linear Regressors

HYPERSIM

Clusters

Photovoltaic Systems

The present paper focuses on the optimization of maximum power point tracking (MPPT) in photovoltaic systems using a linear regression approach. The main objective is to develop an MPPT algorithm using linear regression techniques to improve the accuracy in identifying and tracking the maximum power point. The proposed algorithm is developed in MATLAB/Simulink software and validated through experimental tests. Subsequently, the application of the algorithm is extended to an electrical network modeled and simulated in the HYPERSIM tool environment, this software will allow to address in a more detailed and accurate way the instantaneous dynamics of electrical and control variables in complex systems, through the variation of variables such as temperature and irradiation.

The innovative contribution of this project is not only limited to the improvement of MPPT algorithms, but also comprehensively addresses the integration of renewable energies in electrical systems. The effectiveness of the linear regression-based algorithm represents a crucial advance in maximizing control efficiency and response in photovoltaic systems. Optimizing the conversion of solar energy into usable electricity not only increases the cost-effectiveness and sustainability of these systems, but also highlights the critical role they play in the transition to a more sustainable electricity supply.

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