Enhancing Photovoltaic System Performance: A Comparative Study of AI-Based Neural Networks and Traditional MPPT Techniques
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Keywords:
Photovoltaic (PV) Systems, Perturb And Observe (P&O) Method, Neural Networks (NN), Maximum Power Point Tracking (MPPT), Buck-Boost ConverterAbstract
The increasing reliance on renewable energy emphasizes the critical need for optimizing
photovoltaic (PV) systems to achieve maximum energy output. Traditional Maximum Power Point
Tracking (MPPT) methods, such as Perturb and Observe (P&O), are commonly used due to their
simplicity. However, they encounter challenges like oscillations around the Maximum Power Point
(MPP) and slower adaptation under dynamic environmental conditions. This study addresses these
limitations by evaluating AI-based MPPT techniques, particularly Neural Networks (NN), in comparison
to the P&O method, highlighting their superior adaptability and efficiency. Using MATLAB simulations,
the study analyzes the performance of these methods in an independent PV system featuring a solar array,
buck-boost converter, and variable resistive load. Results reveal that AI-based MPPT approaches,
especially Neural Networks, deliver smoother power outputs, faster convergence to the MPP, and reduced
stress on PV components. By leveraging real-time and historical data, these techniques demonstrate
enhanced predictive capabilities, making them highly suitable for regions with fluctuating environmental
conditions.
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References
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