Abstract
One of the most reliable and advanced renewable energy sources is wind energy. It is critical to harness as much wind energy as possible and maintain wind turbines operating at full capacity. Maximum power point tracking (MPPT) is a cutting-edge study that incorporates a variety of approaches. Because each MPPT technique has its own set of advantages and disadvantages, developing an accurate maximum power point tracking methodology for a certain case necessitates understanding. As a result, they must be checked thoroughly. This research tries to examine many algorithms that can be used to improve the wind energy system's global MPPT performance. The traditional "Perturb and Observe " tool, the optimization method based on the "particle swarm optimization algorithm, " the neural network, and the "fuzzy logics " as intelligent tools are these techniques. The main objective of this research is to define and evaluate four different flexible algorithms that achieve the fundamental objective of this optimization. The advantages, drawbacks, and thorough analysis of MPPT systems are highlighted in terms of initial investment, responsiveness, and capacity to create maximum energy output. All of this comparison was made through simulation software, which is the MATLAB Simulink tool. The conclusions are supported by a comprehensive discussion and presentation of the results for a variety of situations and tests that reflect real-world behavior in any wind system.