An Evaluation of Maximum Power Point Tracking Methods in Photovoltaic Systems with AHP and Shannon Entropy

• Published in: 2023 4th International Conference on Advanced Electrical and Energy Systems (AEES) pp. 727 - 732
• Main Authors: Mamani, James Rolando Arredondo, Huayta, Ivan Delgado
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2023
• Subjects: AHP; Classification algorithms; DC-DC power converters; Entropy; Fuzzy logic; Maximum power point trackers; MPPT; Neural networks; qualification; Solar panels
• DOI: 10.1109/AEES59800.2023.10469216

This Paper evaluates some algorithms of Maximum Power Point Tracking (MPPT) in solar photovoltaic (PV) systems, these algorithms are very extensive and use different techniques, from basic algorithms to some algorithms based on artificial intelligence. These algorithms aim to find the Maximum Power available from the Solar Panel of a photovoltaic system, which consists of a solar panel connected to a direct current to direct current (DC-DC) power converter. Since there are several MPPT algorithms and therefore the question of which one to use may arise, five of these are compared with their respective characteristics, these algorithms are the most used in the literature. For the comparison, multi-criteria techniques were used, these techniques assign ranking values according to their characteristics, for this case the Entropy by weight method (EW) based on the Shannon Entropy and Analytic Hierarchy Process (AHP) were used; At the end of this paper, a rating table of the five MPPT algorithms for each multicriteria technique is shown and both classification methods are compared.