Comparative Analysis of Maximum Power Point Tracking (MPPT) Techniques for Grid-Connected Photovoltaic Systems: Perturb and Observe, Artificial Neural Network, and Fuzzy Logic Control Algorithms with using Real Weather Data in MATLAB Simulink

The increasing global demand for renewable energy has led to significant advancements in photovoltaic (PV) systems. As a clean and sustainable energy source, PV technology plays a crucial role in reducing reliance on fossil fuels and mitigating environmental impacts [1]. However, the efficiency of PV systems is highly influenced by external factors such as solar irradiance and temperature variations. Since PV modules exhibit nonlinear power-voltage characteristics, Maximum Power Point Tracking (MPPT) techniques are essential for optimizing energy conversion efficiency [2]. This thesis presents the design and simulation of a photovoltaic (PV) panel system using real-time weather data, focusing on the implementation of three Maximum Power Point Tracking (MPPT) algorithms: Perturb and Observe (P&O), Fuzzy Logic Control (FLC), and Artificial Neural Networks (ANN). The weather data, such as solar irradiance and temperature, is collected through real-time datas and fed into the simulation model. This ensures that the algorithms are tested under conditions that mimic real-world scenarios. To address this challenge, various MPPT techniques have been developed. This thesis evaluates and compares three MPPT methods—Perturb and Observe (P&O), Artificial Neural Network (ANN), and Fuzzy Logic Control (FLC)—in a grid-connected PV system. The P&O algorithm is widely used due to its simplicity but suffers from oscillations and slow response times [3]. In contrast, ANN-based MPPT controllers leverage machine learning to predict optimal operating points, offering faster and more accurate tracking [4]. Similarly, FLC-based controllers utilize rule-based decision-making to handle uncertainties and nonlinearities effectively, ensuring stable performance under dynamic conditions [5]. The ANN implementation achieved remarkable accuracy metrics, with a global accuracy ranging from 98.32% to 98.09% and an average accuracy of 99.60%, demonstrating the method's reliability in duty cycle prediction. The transient behaviour of the system under the FLC-based controller was rapid and stable. The system exhibited a quick initial response and rapid convergence to the steady-state MPP, even under dynamic environmental conditions. This improvement in transient behaviour highlights the FLC's ability to adapt quickly to changes in irradiance and temperature. The P&O algorithm demonstrates effective tracking of the MPP under steady-state conditions, but its performance is limited by oscillations around the MPP, moderate efficiency, and slow tracking speed under dynamic conditions. These limitations suggest that while the P&O method is suitable for basic MPPT applications, there is room for improvement in terms of efficiency and responsiveness, particularly in environments with rapidly changing irradiance and temperature. [1] K. Hansen, B. V. Mathiesen, and H. Lund, "The role of photovoltaics in future energy systems: Integrating solar energy into sustainable solutions," Renewable and Sustainable Energy Reviews, vol. 119, p. 109541, 2020. doi: 10.1016/j.rser.2019.109541. [2] T. Esram and P. L. Chapman, "Comparison of photovoltaic array maximum power point tracking techniques," IEEE Transactions on Energy Conversion, vol. 22, no. 2, pp. 439–449, 2007. doi: 10.1109/TEC.2006.874230. [3] V. Salas, E. Olias, A. Barrado, and A. Lazaro, "Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems," Solar Energy Materials and Solar Cells, vol. 90, no. 11, pp. 1555–1578, 2006. doi: 10.1016/j.solmat.2005.10.023. [4] R. Sharma and S. Nema, "A review on MPPT techniques for photovoltaic systems," Renewable and Sustainable Energy Reviews, vol. 78, pp. 693–709, 2017. doi: 10.1016/j.rser.2017.04.118. [5] A. Mellit and S. A. Kalogirou, Renewable and Sustainable Energy Reviews, vol. 50, pp. 1186–1207, 2014.