An Efficient Maximum Power Extraction Algorithm for Wind Energy Conversion System Using Model Predictive Control

This paper analyses the performance comparison of maximum power point tracking (MPPT) algorithms for a wind energy conversion system using a model predictive control (MPC). Perturb & Observe (P&O) and Incremental Conductance (INC) that are the most commonly used maximum power point tracking (MPPT) methods due to their simple structure and facility to implement. However, both have the limitation in the effective tracking of maximum power point under unpredictable and fluctuating nature of wind turbine systems. In order to overcome the above limitations a model predictive control technique is proposed in order to enhance energy efficiency of wind turbine systems during rapidly changing wind conditions. MPC is an optimization method, it offers fast tracking, low power oscillations in steady state as encountered with the conventional methods. The proposed method uses variable predictive perturbation step size determined by Newton-Raphson method. Compared to traditional P&O and INC, the proposed strategy converges to maximum power point more rapidly and reduces the steady state power oscillations around maximum power point (MPP). The effectiveness of the control scheme is validated using MATLAB/SIMULINK simulation studies and through a scaled laboratory model using dSPACE DS1007 platform in order to verify the simulation results.