Implementation of Advanced Nonlinear Controllers for MPPT Control of Two Mass Wind Turbine and Indirect Power Control of DFIG using HOSMC and ANN

This paper aims to study advanced controls of two-mass wind turbine (WT) system based on a doubly fed induction generator (DFIG) to improve their performance. This work has two main objectives, which are the maximum aerodynamic power extraction from wind energy and indirect power control of DFIG. In the matter of two mass wind turbine control, the tip speed ratio (TSR) method based on maximum power point tracking algorithm (MPPT) is used to extract maximum power from kinetic energy of wind by implementing a high order sliding mode controller (HOSMC) to control the generator speed. Afterwards, the active and reactive power of DFIG is controlled by using HOSMC and artificial neural network (ANN) to evaluate their impact on the performance of DFIG wind turbine system. The dataset obtained from the SMC is used to train the proposed ANN architecture using a back-propagation algorithm. After training, the HOSMC is replaced with a constructed ANN design into DFIG power control loops. The obtained results illustrate that the two-mass WT system extracts maximum power from the wind energy by obtaining maximum coefficient power, and optimal TSR, additionally, the generator speed is well tracked the reference value. Furthermore, the ANN and HOSM controllers highlight effectiveness in the DFIG indirect power control by tracking the references values of the active and reactive power. However, ANN controller yielded superior results compared to the others by significantly reducing overshoot and improving time response, which can improve the performance of two-mass WT system.