A novel strategy for the MPPT in a photovoltaic system via sliding modes control

• Published in: PloS one 2024-12, Vol.19 (12), p.e0311831
• Main Authors: Contreras Carmona, Itzel, Saldivar, Belem, Portillo-Rodríguez, Otniel, Ramírez Rivera, Víctor Manuel, Gil Antonio, Leopoldo, Jacinto-Villegas, Juan Manuel
• Format: Article
• Language: English
• Subjects: Algorithms; Alternative energy sources; Artificial neural networks; Bayes Theorem; Bayesian analysis; Computer Simulation; Control systems; Controllers; Efficiency; Electric Power Supplies; Energy conversion efficiency; Incremental conductance; Irradiance; Management; Mathematical models; Maximum power tracking; Methods; Models, Theoretical; Multiple regression models; Neural networks; Neural Networks, Computer; Numerical simulations; Optimization; Oscillations; Photovoltaics; Proportional integral derivative; Regression analysis; Regression models; Regularization; Renewable resources; Robust control; Sliding mode control; Solar Energy; Solar irradiance; Solar radiation; Sustainable development; Temperature; Temperature requirements; Temperature sensors; Tracking control; Tracking problem
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0311831

This paper proposes a robust maximum power point tracking algorithm based on a super twisting sliding modes controller. The underlying idea is solving the classical trajectory tracking control problem where the maximum power point defines the reference path. This trajectory is determined through two approaches: a) using the simplest linear and multiple regression models that can be constructed from the solar irradiance and temperature, and b) considering optimum operating parameters derived from the photovoltaic system's characteristics. The proposal is compared with the classical methods Perturbation and Observation and Incremental Conductance, as well as with two recently reported hybrid algorithm based on Artificial Neural Networks: one uses the Levenberg-Marquardt algorithm and the other applies Bayesian regularization to generate current and voltage references, respectively. Both use a Proportional-Integral-Derivative controller to solve the maximum power point tracking problem. Numerical simulations confirm the effectiveness of the method proposed in this work regarding convergence time, power efficiency, and amplitude of oscillations. Furthermore, it has been shown that, although no significant differences in the system response are observed with respect to the Artificial Neural Networks-based methods, the proposed algorithm with a reference generated through a linear regression constitutes a low-complexity solution that does not require a temperature sensor to efficiently solve the maximum power point tracking problem.