Utilizing Fuzzy Logic to Optimize the Extraction of Maximum Output Power from the Turbine

In recent decades, wind energy systems have gained prominence as a significant source of renewable energy. This rise in importance can be attributed to the diminishing reserves of fossil fuels, escalating costs, and the adverse environmental effects associated with conventional energy sources. A notable challenge within this technology is the development of controllers that exhibit optimal performance for variable speed wind turbine systems. Variable speed wind turbines generate varying levels of output power. Without the implementation of an effective control strategy aimed at maximizing the turbines' power efficiency, substantial amounts of energy can be squandered. Consequently, it is imperative to mitigate this energy loss and employ suitable methods to attain the highest possible power output from variable speed wind turbines. In recent years, a variety of techniques have been explored to achieve optimal control of wind turbines and maximize their power extraction. Each of these techniques presents its own set of advantages and disadvantages. This research begins with an examination of the structure and types of wind turbines, followed by the application of a fuzzy controller to enhance power generation. Subsequently, another fuzzy logic application, known as adaptive neuro-fuzzy inference system (ANFIS), is utilized in the controller design, and the outcomes are compared with those obtained using the proportional-integral (PI) method. Finally, the performance of these controllers is assessed through simulations conducted on a wind turbine equipped with a permanent magnet generator.