This paper discusses the performance of various intelligent control schemes in extracting maximum wind power using doubly fed induction generator (DFIG). Intelligent control scheme such as fuzzy, neuro-fuzzy, and genetic algorithm based fuzzy controllers are applied for pitch control of DFIG based wind generation system. Wind generation system with eight numbers of identical 1.5MW wind generators with reactive and real load is considered. Performance of various intelligent controllers is compared with PID controllers. Simulation results show that the performance of intelligent controllers better than PID controllers and in particular GA based fuzzy controller is better than other intelligent controllers.
The neuro fuzzy controller makes the wind turbine speed to be tuned fine until it gets the error free output which the user need. In this paper, neural networks based controller is used in a narrative approach to solve the problem of tuning a fuzzy logic controller. The membership function of neuro fuzzy logic follows the neural network learning techniques to tune the membership function.By 2030, wind energy will be the most cost-efficient energy resources on the market. On the other hand, with the growing demand for green electricity worldwide today, the turbine costs raises rapidly. Also the competition for supplying ever green power to the grid, the wind farm operators have to improve their existing power output. In this, we predict the extraction of power through controller based techniques. The scope includes the simulation study, implementation of Neuro-fuzzy logic controller using MATLAB simulator.
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