A COMPARATIVE STUDY OF PHOTOVOLTAIC MAXIUM POWER POINT TRACKING ALGORITHMS UNDER DYNAMIC WEATHER CONDITIONS

Based on a literature review of various MPPT algorithms, it can be concluded that each algorithm has its own advantages and limitations depending on the operational conditions of the photovoltaic system. Conventional algorithms such as Perturb and Observe (P&O) and Incremental Conductance (INC) offer a simple structure and easy implementation, but are less responsive to rapid weather changes. Meanwhile, artificial intelligence-based algorithms such as Fuzzy Logic Control (FLC), Artificial Neural Network (ANN), and Particle Swarm Optimization (PSO) demonstrate superior performance in terms of tracking speed, efficiency, and stability under dynamic conditions. The combination of algorithms or hybrid methods has also been proven to improve system resilience to irradiance and temperature fluctuations. Therefore, the selection of an MPPT algorithm must consider the context of use, such as environmental conditions, hardware capacity, and the overall efficiency needs of the system. With the right approach, MPPT systems can significantly increase the power output of solar panels and support sustainable energy efficiency.