Sliding Mode of Second-Order Control Based on Super-Twisting ANFIS Algorithm of Doubly Fed Induction Generator in Wind Turbines Systems with Real Variable Speeds

The current study of robust sliding mode second-order controller (SMSOC) based on super-twisting adaptive neuro-fuzzy inference system (ST-ANFIS) algorithm applied to an HWTS (horizontal wind turbine system) with doubly fed induction generator (DFIG) was carried out under real variable wind speed proposed. The DFIG stator is connected directly to the main grid, while its rotor is coupled to the network via a Back-to-Back converter. Fuzzy logic and neural networks are important branches of artificial intelligence strategies and are often used simultaneously. The difficulties and inherent limitations of each individual technique are the motivations for combining these two strategies with the name of adaptive neuro-fuzzy inference systems (ANFIS). The super-twisting ANFIS algorithm used in SMSOC was employed to maximize the captured wind energy due to its capacity to reduce the chattering problem caused by function switching of conventional SMSOC. It can also be used to enhance the quality of the power supply of the grid against parametric variations. The simulation test was used to verify the proposed approach's (ST-ANFIS) improved performance and robustness over the classical strategy of sliding mode second-order and SMSO based on super-twisting fuzzy (ST-Fuzzy), in terms of chattering issue and then the response to speed wind variations and high robustness against variations of machine parameters. Hence, the enhancement of power quality supply of grid is included in the comparative study.