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4 Abstract - Wind energy has steadily established itself as one of the most reliable and affordable renewable energy resources. Of the country's wind energy generation capacity of around 14,000MW per year, TN accounts for 6,547MW. Tamil Nadu has an installed wind energy capacity of 6,696 MW, which is 41 per cent that of the country. The paper presents an advanced prediction system that integrates models based on fuzzy-neural networks. The main objective of this paper is to fully control the wind turbine system with induction generator to maximize the power generation. The output power of a wind turbine is function of wind velocity cubed. The system has been optimized for operating around 15 m/s, and though the wind power continues to increase above this point. The advanced method to introduce the new ability of estimating variable speed control . By using the controller, the speed can be tuned until it get similar to the desired output that user need. In this paper, neural networks are used in a novel way to solve the problem of tuning a fuzzy logic controller. The neuron fuzzy controller uses the neural network learning techniques to tune the membership function. The aim is to ensure that by 2030, wind energy will be the most cost-efficient energy source on the market. However, with the growing demand for green electricity worldwide, rising turbine costs and increased competition to supply green electricity to the grid, wind farm operators must improve their existing power output. In India, the total installed capacity of wind power generation is 8754 MW in the year 2008.By the end of 2012, the total installed capacity is going to be reached to 12000 MW according to ministry of new and renewable energy, India and total installed capacity of wind energy is estimated to be more than 160 GW (WWEA) all around the world. The scopes include the simulation study, implementation of Neuron fuzzy logic controller to actual DC motor using MATLAB simulator.

Extracting Maximum Power Optimization in Wind Turbine Using Neuro Fuzzy Logic Control