A Hybrid RBFNN-PI MPPT Controller for Optimal Energy Harvesting in PV Systems under Changing Environmental Conditions
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Abstract
Traditional maximum power point tracking (MPPT) techniques often struggle to adapt to unpredictable weather variations, limiting their efficiency under dynamic environmental conditions (irradiance and temperature). To address this challenge, this study proposes a novel hybrid MPPT controller that combines a Radial Basis Function Neural Network (RBFNN) with a Proportional-Integral (PI) control strategy, thereby enhancing adaptability and responsiveness. The system is modeled and simulated in MATLAB/Simulink, with the PV array's current, voltage, and power outputs rigorously validated against manufacturer specifications to ensure simulation accuracy. The proposed RBFNN-PI-based MPPT controller achieves an exceptional tracking efficiency of 99% with a rapid dynamic response time of 0.02 seconds. Simulation results confirm the method's superior performance, demonstrating high precision, minimal steady-state oscillations, and fast convergence even under rapidly fluctuating irradiance and temperature conditions. By integrating RBFNN's intelligent prediction with PI's robust regulation, the controller ensures reliable and efficient power extraction. This approach presents a promising solution for maximizing energy harvest in PV systems, advancing the development of more sustainable and resilient solar energy applications.
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