Please use this identifier to cite or link to this item:
http://hdl.handle.net/10174/38973
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Title: | Optimizing MPPT: A Comparative Study of P&O, Predictive Control, Fuzzy Logic, and Neural Network Methods |
Authors: | Ali, Md Suruj Rashel, Masud Rana Ahmed, Md Tofael Tlemçani, Mouhaydine |
Keywords: | MPPT P&O Predictive Control Fuzzy Logic Artificial Neural Network Photovoltaic Systems Renewable Energy |
Issue Date: | Jul-2024 |
Publisher: | UNIVERSIDADE DE ÉVORA |
Abstract: | Optimizing the energy efficiency of solar PV panels is pivotal for advanced energy harvesting and utilization. Solar PV panels energy generation depends on environmental parameters such as irradiance and temperature which can increase the maximum power point tracking (MPPT) algorithm complexity due to its nonlinear characteristics. This study investigates a comparative analysis of four MPPT algorithms based on P&O, Predictive Control Method, Fuzzy Logic, and Artificial Neural Network (ANN) [1]. A robust boost converter is designed along with a classical P&O-based MPPT algorithm and compares
the simulation results with artificial intelligence-based MPPT algorithms. The P&O technique
is evaluated with the predictive control method which leverages next-state predictions
towards enhancing accuracy. The study indicates Fuzzy Logic can manage system uncertainties
along with adaptive decisions, while Artificial Neural Networks (ANN) explore
the potential to learn dynamically and adapt to changing conditions [2–4]. The simulation
shows the variation in response time, efficiency, and stability across the algorithms. This
comparative study provides a guide to selecting the most effective and efficient MPPT
algorithms to elevate the performance and reliability of solar PV systems [5,6]. |
URI: | https://www.2iwmps24.uevora.pt/ http://hdl.handle.net/10174/38973 |
Type: | lecture |
Appears in Collections: | CREATE - Comunicações - Em Congressos Científicos Nacionais
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