Adaptive Neural Network-Based Voltage Regulation for a High-Gain Boost Converter in Solar Photovoltaic Systems

Abstract

This study proposes an adaptive Artificial Neural Network-based voltage control strategy for maintaining a stable DC bus voltage in a high-gain DC-DC boost converter for solar photovoltaic systems. Unlike conventional PID controllers, which struggle with non-linear and dynamic conditions, the proposed controller dynamically adjusts the duty cycle to mitigate the effects of varying solar irradiance and reference voltage, ensuring robust voltage regulation with reduced overshoot, enhanced transient response, and improved steady-state stability. This approach addresses critical challenges in standalone solar applications, such as water pumping and rural electrification, where consistent performance is essential despite fluctuating environmental conditions. In comparison to conventional control strategies, the ANN-based controller demonstrates superior adaptability, particularly under rapidly changing operating conditions. The results demonstrate the superior adaptability and efficiency of the ANNbased controller compared to the conventional PID controller, making it a valuable and reliable solution for sustainable solar PV systems. The proposed system was validated using a cosimulation framework that integrates MATLAB/Simulink and OrCAD, facilitating performance evaluation under varying solar irradiance and reference voltage conditions.