Paper Title
AN INTELLIGENT PHOTOVOLTAIC DESIGNED MAXIMUM POWER POINT TRACKING CONTROLLER SYSTEM

Abstract
This paper presents a novel approach to enhance the performance of solar photovoltaic (PV) systems through the integration of an Artificial Neural Network (ANN) into a Proportional-Integral-Derivative (PID) based Maximum Power Point Tracking (MPPT) controller. MPPT techniques are widely employed to extract the maximum available power from solar panels, improving the overall energy efficiency of PV systems. However, conventional MPPT methods often face challenges in accurately tracking the Maximum Power Point (MPP) due to environmental variations and system non-linearities. The proposed approach addresses these limitations by incorporating an ANN into the control loop of a PID-based MPPT controller. The ANN is trained using historical solar irradiance and temperature data to learn the non-linear characteristics of the PV system. By utilizing the learned knowledge, the ANN enhances the decision-making capabilities of the MPPT controller, enabling it to adapt to changing environmental conditions and improve tracking accuracy. To validate the effectiveness of the proposed ANN-integrated PID MPPT controller, extensive simulations and experimental tests are conducted. The results demonstrate significant improvements in the tracking efficiency and overall performance of the solar PV system compared to traditional PID-based MPPT methods. The ANN-based approach exhibits enhanced response time, reduced oscillations, and improved power extraction capabilities under varying solar irradiance and temperature conditions. Furthermore, the paper explores the impact of different ANN architectures and training algorithms on the controller's performance, facilitating an understanding of the optimal configuration for the specific PV system. The findings contribute to the field of solar energy research by providing a novel and effective approach for MPPT control, which can be implemented in real-world solar PV systems to enhance their energy harvesting capabilities and promote sustainable energy generation. Keywords - Artificial Neural Network, Maximum Power Point Tracking, PID controller, Renewable energy, Solar PV systems.