article · Frontiers in Energy Research
Generating the maximum possible energy from wind turbines requires effective control systems that track peak power points during operation. This research assesses four different maximum power point tracking methods designed to boost energy capture in wind conversion systems. The evaluated approaches comprise the traditional Perturb and Observe method alongside intelligent optimisation techniques including particle swarm optimisation, artificial neural networks, and fuzzy logic. Using MATLAB Simulink simulations, each algorithm was tested under conditions designed to reflect realistic wind system behaviour. The comparative evaluation weighs key trade-offs between the techniques, specifically examining differences in required initial investment, system responsiveness, and total capacity to produce maximum energy output across diverse operating scenarios.
Wind energy is a critical component of renewable power generation, but turbines must operate efficiently under varying wind conditions. Understanding how different tracking algorithms perform helps engineers select the most appropriate control methods to maximise electricity generation while balancing setup costs and response speeds.
This work can guide wind turbine manufacturers and control system developers in selecting suitable tracking algorithms for turbine controllers. Because the findings rely entirely on MATLAB Simulink simulations without hardware trials mentioned in the abstract, the research sits at an early, computer-modelled stage prior to physical prototype testing or deployment.
AI-generated from the published abstract. Always read the original work before citing.
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.
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DOI: 10.3389/fenrg.2022.975134
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