RAPID PROTOTYPING OF AN ARTIFICIAL NEURAL NETWORK-BASED MAXIMUM POWER POINT TRACKING CONTROLLER FOR PHOTOVOLTAIC SYSTEMS

Abstract

The increasing demand for clean and sustainable energy has led to the widespread adoption of photovoltaic (PV) systems for electricity generation. However, the power generated by PV modules is greatly influenced by changes in solar irradiance and temperature, resulting in variations in output voltage and power. To improve the efficiency of photovoltaic systems, Maximum Power Point Tracking (MPPT) techniques are employed to ensure that the PV module operates at its maximum power point under different environmental conditions. Artificial Neural Networks (ANNs) have gained popularity as intelligent MPPT techniques because of their ability to learn the nonlinear characteristics of PV systems and provide faster and more accurate tracking than conventional methods. Nevertheless, the development of ANN-based controllers can be time-consuming if conventional design approaches are used. Rapid prototyping provides an efficient solution by integrating modelling, simulation, testing, and validation before hardware implementation. This paper discusses the application of rapid prototyping in the development of an ANN-based MPPT controller for photovoltaic systems. It presents the concepts of rapid prototyping, ANN-based MPPT, the proposed development framework, software tools used, and the benefits and challenges associated with the methodology. The paper concludes that rapid prototyping significantly simplifies controller development, reduces implementation time, minimizes design errors, and improves the overall performance and reliability of intelligent photovoltaic control systems.