Photovoltaic systems: Analytic comparison of fuzzy logic and ML methods for applying maximum power tracking systems

Document Type

Conference Proceeding

Publication Title

International Conference on Robotics, Electrical and Signal Processing Techniques

First Page

222

Last Page

227

Publisher

IEEE

School

School of Engineering

Publication Unique Identifier

10.1109/ICREST63960.2025.10914370

Comments

Ahmed, M. U., Qays, O., Lachowicz, S., Thundiyil, N. T., Hossain, M. L., Amin, U., ... & Yasmin, F. (2025, January). Photovoltaic systems: Analytic comparison of fuzzy logic and ML methods for applying maximum power tracking systemss. In 2025 4th International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) (pp. 222-227). IEEE. https://doi.org/10.1109/ICREST63960.2025.10914370

Abstract

Integration of artificial intelligence (AI) in solar power systems for maximum power point tracking (MPPT) is increasingly popular due to the limitations of traditional MPPT methods in locating the global maximum power point (GMPP) under partial shading conditions. Unlike conventional techniques, AI-based algorithms excel at identifying the GMPP even when multiple local maximum power points (MPPs) exist. Compared to traditional methods, AI-based MPPT techniques like reinforcement learning and fuzzy logic typically offer higher efficiency, reduced steady-state oscillation, and faster convergence but require significant resources and investment. This paper compares two AI-based MPPT methods-Fuzzy Logic and Reinforcement Learning using simulation. Each AI approached its strengths and weaknesses, complicating on optimal method selection. It provided a detailed efficiency comparison of these AI methods by implementing them in a solar power grid system under various environmental conditions.

DOI

10.1109/ICREST63960.2025.10914370

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