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

The technological development of wind energy conversion systems (WECS) is emphasized on the injection of wind power into the utility grid more smoothly and robustly. Sliding mode control (SMC) has proven to be a popular solution for the grid-connected WECS due to its robust nature. The super twisting sliding mode control (STSMC), a variant of SMC, is an effective approach to suppress the inherent chattering in SMC and provide error-free control. The anti-disturbance capabilities of STSMC deteriorate due to the non-linear part that is based on variable approaching law and 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 WECS is proposed. Then, an artificial neural network (ANN)-based compensation term is added to improve the convergence and anti-disturbance capability 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.