Convergence Enhancement of Super-Twisting Sliding Mode Control Using Artificial Neural Network for DFIG-Based Wind Energy Conversion Systems

The smooth and robust injection of wind power into the utility grid requires stable, robust, and simple control strategies. The super-twisting sliding mode control (STSMC), a variant of the sliding mode control (SMC), is an effective approach employed in wind energy systems for providing smooth power transfer, robustness, inherent chattering suppression and error-free control. The STSMC has certain disadvantages of (a) less anti-disturbance capabilities due to the non-linear part that is based on variable approaching law and (b) time delay created by the disturbance and uncertainties. This paper enhances the anti-disturbance capabilities of STSMC by combining the attributes of artificial intelligence with STSMC. Initially, the STSMC is designed for both the inner and outer loop of a doubly fed induction generator (DFIG) based wind energy conversion system (WECS). Then, an artificial neural network (ANN)-based compensation term is added to improve the convergence and anti-disturbance capabilities of STSMC. The proposed ANN based STSMC paradigm is validated using a processor in the loop (PIL) based experimental setup carried out in Matlab/Simulink.