Authors:
João T. Sousa
1
and
Ramiro S. Barbosa
2
;
1
Affiliations:
1
Department of Electrical Engineering, Institute of Engineering – Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal
;
2
GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, ISEP/IPP, 4249-015 Porto, Portugal
Keyword(s):
MPPT, Photovoltaic Systems, Fuzzy Logic Controller, Genetic Algorithm, P\&O, Incremental Conductance, Solar Energy.
Abstract:
This paper presents a comparative study of five MPPT (Maximum Power Point Tracking) algorithms applied to photovoltaic (PV) systems under both uniform and dynamic environmental conditions. The analyzed algorithms include two conventional methods, Perturb \& Observe (P\&O) and Incremental Conductance (InC), as well as a fuzzy logic controller (FLC) and two hybrid strategies enhanced by genetic algorithms (P\&O+GA and InC+GA). A unified simulation framework in MATLAB/Simulink was used to ensure fair benchmarking, employing identical panel configurations, irradiance/temperature profiles, and converter parameters. Each algorithm was tested using predefined parameters such as step size, initial duty cycle, and operating bounds. Additionally, an EMA (Exponential Moving Average) filter was applied to the hybrid algorithms to reduce high-frequency measurement noise. Evaluation metrics include Mean Absolute Error (MAE), Integral Absolute Error (IAE), Mean Squared Error (MSE), Integral Squared
Error (ISE), convergence time, and energy conversion efficiency. Results demonstrate that hybrid methods deliver superior performance in noisy and fast-changing conditions, while FLC maintains stable performance with reduced oscillations. This work aims to support the selection of suitable MPPT techniques for real-world PV systems, balancing computational complexity and control effectiveness.
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