Rotor Current Control Design for DFIG-based Wind Turbine Using PI, FLC and Fuzzy PI Controllers

Due to the rising demand for electricity with increasing world population, maximizing renewable energy capture through efficient control systems is gaining attention in literature. Wind energy, in particular, is considered the world’s fastest-growing energy source it is one of the most efficient, reliable and affordable renewable energy sources. Subsequently, well-designed control systems are required to maximize the benefits, represented by power capture, of wind turbines.In this thesis, a 2.0-MW Doubly-Fed Induction Generator (DFIG) wind turbine is presented along with new controllers designed to maximize the wind power capturer. The proposed designs mainly focus on controlling the DFIG rotor current in order to allow the system to operate at a certain current value that maximizes the energy capture at different wind speeds. The simulated model consists of a single two-mass wind turbine connected directly to the power grid. A general model consisting of aerodynamic, mechanical, electrical, and control systems are simulated using Matlab/Simulink. An indirect speed controller is designed to force the aerodynamic torque to follow the maximum power curve in response to wind variations, while a vector controller for current loops is designed to control the rotor side converter.The control system design techniques considered in this work are Proportional-Integral (PI), fuzzy logic, and fuzzy-PI controllers. The obtained results show that the fuzzy-PI controller meets the required specifications by exhibiting the best steady-state response, in terms of steady-state error and settling time, for some DFIG parameters such as rotor speed, rotor currents and electromagnetic torque. Although the fuzzy logic controller exhibits smaller peak overshoot and undershoot values when compared to the fuzzy-PI, the peak value difference is very small, which can be compensated using protection equipment such as circuit breakers and resistor banks. On the other hand, the PI controller shows the highest overshoot, undershoot and settling time values, while the fuzzy logic controller does not meet the requirements as it exhibits large, steady-state error values.