Performance comparison of power point tracking algorithms in photovoltaic system

In this study, renewable energy systems, which have attracted significant attention in recent years due to their environmentally friendly nature and capability to provide sustainable energy production, are examined. However, the inherently variable and unpredictable generation characteristics of these systems can lead to voltage fluctuations and instabilities in electrical grids, thereby adversely affecting energy continuity and posing a threat to grid stability. To address this issue, the integration of energy storage units into renewable energy systems is considered an effective solution for mitigating production–consumption imbalances and enhancing overall system stability. Within this context, a photovoltaic (PV) energy system integrated with an energy storage unit has been designed, and the performances of various Maximum Power Point Tracking (MPPT) algorithms have been investigated to ensure the system operates with maximum efficiency. Both deterministic and stochastic optimisation-based methods were evaluated, and their effects on the system's dynamic behaviour, response time, stability, and energy efficiency were analysed. Consequently, the contributions of different MPPT strategies to the overall performance of PV systems were comprehensively demonstrated. In the proposed system, photovoltaic panels are connected to a 96 V DC bus through a DC/DC converter. To enhance voltage stability and store excess generated energy, a lithium-based battery pack is integrated into the system via a bidirectional DC/DC converter. This configuration ensures both energy continuity and voltage stability, thereby improving the overall system efficiency. Four different algorithms were employed for maximum power point tracking: Perturb and Observe (P&O), Incremental Conductance (INC), Fuzzy Logic-Based Perturb and Observe (FL-P&O), and Particle Swarm Optimisation (PSO). These algorithms were compared based on performance indicators such as rise time, settling time, and overshoot ratio. Simulation results revealed that the FL-P&O algorithm exhibited the best performance, achieving a rise time of 14.28 ms, a settling time of 51.6 ms, and a battery state-of-charge of 69.96%. These findings demonstrate that the fuzzy logic-based approach provides superior response speed and higher system stability. Simulation analyses conducted in the MATLAB/Simulink environment confirmed that the proposed system maintains stable performance under varying environmental conditions. Furthermore, real solar irradiance data collected over a 24-hour period were utilised to evaluate the system's adaptability to real operating conditions. The results indicate that artificial intelligence-based MPPT algorithms offer greater accuracy, faster response, and enhanced stability compared to conventional methods in PV systems with energy storage, highlighting their potential significance in the development of future intelligent energy management systems. 2025, ix + 57 pages Keywords: Renewable Energy, Solar Energy, Maximum Power Point Tracking, Fuzzy Logic Controller, Incremental Conductance, Particle Swarm Optimisation.