Design and Analysis of Rate Predictive Fractional-Order Sliding Mode Controller (RP-FOSMC) for MPPT and Power Regulation of DFIG-based Wind Energy Conversion System (WECS)

Abstract

Now a days, maximum power point tracking (MPPT), power regulation and transient load reduction are the major issues in the WECSs. To overcome these challenges and to cope with WECS nonlinearity and MIMO nature, nonlinear predictive control algorithms based on Tip-Speed Ratio MPPT (TSR-MPPT) and Rate Predictive Control (RPC) strategies are proposed for the variable speed DFIG-based WECS. The designed control system consists of a FOSMC that is based on fractional-order PID sliding surface model and super-twisting control algorithm (STA) for MPPT through Optimal Torque Control (OTC) algorithm and a Nonlinear-RPC (N-RPC) that is designed based on STA and RPC strategies for power regulation via pitch angle control. FOSMC based on STA improve the dynamics of the controlled system by increasing the degrees of freedom of the sliding surface, minimizing the effects caused by imperfect modelling of nonlinear systems and provide chattering free response of the system and the stability of the designed STA based FOSMC law was proven by Lyapunov criterion. N-RPC is a novel adaptive model-less feedback control algorithm that addresses the limitations of MPC and PID controllers. It limits the power production above rated wind speed and ensure the transient load reduction. The proposed cascaded control algorithms are implemented in a MATLAB/Simulink environment. In this work, the system parameters of Sany SE7715 wind turbine that is installed in Adama-II wind farm, Ethiopia with generating capacity of 1.5 MW is used. The simulation result confirms that the proposed control algorithms achieve optimum power extraction as compared to second-order SMC (2-SMC) and excellent stress reduction in the drive train under variable wind speed. Moreover, they are effective and robust regarding to the application of different external disturbance and sensor noise (i.e., Gaussian & non-Gaussian) and perfectly achieved the required objectives of our investigation.