International Conference on Emerging Frontiers in Technology for Rural Area (EFITRA) 2012 Proceedings published in International Journal of Computer Applications® (IJCA) 12 Fuzzy PI controller for wind Energy conversion system Neelima V Bhange Asst. Professor, Rajiv Gandhi College of Engineering ,Research and Technology chandrapur .(M.S.) Shridevi A Akkewar Asst. Professor, Rajiv Gandhi College of Engineering ,Research and Technology chandrapur .(M.S.) Priyanka D Chintawar Asst. Professor, Rajiv Gandhi College of Engineering ,Research and Technology chandrapur .(M.S.) U.B. Vaidya, Professor, Rajiv Gandhi College of Engineering ,Research and Technology Chandrapur. ( M.S.) ABSTRACT A Wind Energy Conversion System (WECS) differs from a conventional power system. The power output of conventional system can be controlled where as power output of a WECS depends on the wind. This paper describes fuzzy logic control of induction generator speed in wind turbine application. The aim of fuzzy controller is to established maximum power delivery to the grid from available wind power. Fully-controlled wind turbine which consists of induction generator and back-to-back converter is under estimate. This configuration has full control over the electrical torque, full control of the speed, and also supports reactive power compensation and operation under grid disturbances. Fuzzy logic control algorithm has been applied and validated by detailed simulation in MATLAB/Simulink. All system components have been described in detail. All power system components are simulated in MATLAB software for fuzzy control. For studying the performance of controller ,different abnormal condition are applied even the worst case .simulation result can prove the excellent performance of fuzzy control as improving power quality and stability of wind turbine. Keywords A wind turbine, Doubly Fed Induction Generator, Modelling, simulation, Fuzzy logic controller, wind energy conversion system. 1. INTRODUCTION Renewable energy including solar, wind, tidal, small hydro geothermal, refused derived fuel and fuel cell energy is sustainable, reusable and environmentally friendly and clean. With the increasing shortage in fossil fuel, and pollution problem renewable energy has become an important source .Among the other renewable energy sources wind energy has proven to be one of the most economical one. Earlier constant speed WECS were proposed to generate constant frequency voltages from the variable wind speed .Fuzzy logic. Control of DFIG wind turbine is on the most popular wind turbine which includes an induction generator with slip ring, a partial scale power electronic converter. Fuzzy logic controller is applied to rotor side converter as improving power quality and stability of wind turbine [1].Variable nature of WECS makes it difficult for analysis, design and management. The steady state characteristic of a WECS using doubly fed induction generator analysis is performed to investigate variety of DFIG characteristic, including torque-speed, real and reactive power over speed characteristic [2]. The paper describes a variable speed wind generation system where fuzzy logic principles are used for efficiency optimization and performance enhancement control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which pumps power to a utility grid or can supply to an autonomous system. The system has fuzzy logic control with vector control in the inner loops. A fuzzy controller tracks the generator speed with the wind velocity to extract the maximum power. A second fuzzy controller programs the machine for light load efficiency improvement and a third fuzzy controller gives robust speed control against wind gust and turbine oscillatory torque. The complete control system has been developed, analysed, and validated by simulation study. Performances have then been evaluated in detail[3].An adaptive nonlinear controller for wind energy doubly fed induction machine is based on the feedback linearization technique and includes a disturbance observer for estimate of parameter uncertainties[4].This paper describes the transient behaviour of DFIG driven by the stator disconnect from the grid and grid connection[5,6].energy reliability optimization of wind energy conversion operation can be achieved by the sliding mode control in which a horizontal-axis-grid-connected variable speed DFIG based wind power system and minimizing its mechanical stress[7]. The variable speed wind turbine concept with doubly fed induction generator and partial-scale back-to-back converter on the rotor circuit are most popular. This is due the fact that the partial-scale power converter is typically only 30% of the power fed to the electrical grid. This makes this concept attractive from an economic point of view. 2. WIND POWER *The power in wind is proportional to the cubic wind speed ( v3 ). P/m2 = 6.1 x 10-4 v3 ~ Kinetic energy of an air mass is proportional to v2 ~ Amount of air mass moving past a given point is proportional to wind velocity (v) Set of turbine power curves are given in Fig. 2. It could be noticed that there is the operating point of maximum power delivery for each wind speed. The main goal of the controller is to run wind turbine generator at that operating point. These set of curves could be replaced with only one diagram which gives dependence of wind turbine power related to parameter λ, through turbine power coefficient marked as Cp(λ). Particularly, the wind turbine is characterized by the power coefficient Cp(λ) (see Fig. 3), which is defined as the ratio of actual delivered power to the free stream power flowing through a same but uninterrupted area. The tip speed ratio λ, is the ratio of turbine speed at the tip of a blade to the free stream wind speed. It could be noticed from Fig. 3 that there is the optimal tip speed ratio λopt, which has to be maintained in order to extract maximum power from the wind.