Abstract
The aim of this paper is to study the possibilities of increasing the renewable power of a photovoltaic system through a barely tested bio-inspired algorithm. Photovoltaic energy has a high potential to grows but it has a strong dependence on climate conditions. Particularly, the power production of panels is reduced under partial shading conditions, which is a very common situation in several cities around the world. Therefore, the Maximum Power Point Tracker (MPPT) algorithm becomes critical to control the photovoltaic system. In this paper, we propose a novel MPPT algorithm based on the Artificial Bee Colony (ABC) bio-inspired method and we explicitly define a fitness function based on power production. The ABC algorithm is more attractive than any other bio-inspired methods due to its simplicity and ability to resolve the problem of choosing an ideal duty cycle. Specifically, it requires a reduced number of control parameters and the initial conditions have no influence over the convergence. The analysis has been carried out using real meteorological and consumption data and testing the behavior of the algorithm on a standalone photovoltaic system operating only with direct current.
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Olivares-RodrÃguez, C., Castillo-Calzadilla, T., Kamara-Esteban, O. (2018). Bio-inspired Approximation to MPPT Under Real Irradiation Conditions. In: Del Ser, J., Osaba, E., Bilbao, M., Sanchez-Medina, J., Vecchio, M., Yang, XS. (eds) Intelligent Distributed Computing XII. IDC 2018. Studies in Computational Intelligence, vol 798. Springer, Cham. https://doi.org/10.1007/978-3-319-99626-4_10
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DOI: https://doi.org/10.1007/978-3-319-99626-4_10
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