Blade Angle Control Using TLBO Based Modified Adaptive Controller

Abstract

Wind energy is one of the fastest-growing energy sources of green energy in the world. Research efforts are aimed to address the challenges to greater use of wind energy. Thus, the paper presents a blade angle control based on a new modified adaptive PI controller. The controller relies on teaching learning-based optimization algorithms (TLBO) to optimally evaluate its initials. The effectiveness of the proposed controller is verified by simulation results for six 1.5-MW wind turbines doubly fed induction generator (DFIG) system. The validation composes a comparison with conventional adaptive controller under normal and faulty conditions. The modified adaptive PI showed improved mechanical and electrical behaviors for the wind turbine relying on its second order amplifier. The amplifier works as analogue filter that improves the system dynamic characteristics. The new controller showed robustness to the changes in system parameters and the nonlinearity of the wind turbine systems. The superiority of the new controller has been proved when compared with conventional PID controller.