Implementing an Advanced Neural in the Integral Sliding Mode First Order Controller for Wind Turbine Chains with DFIG

This paper delves into the investigation of an advanced Artificial Neural Network (ANN) Integral Sliding Mode First Order Controller for wind turbine chain a horizontal axis type featuring a Doubly Fed Induction Generator (DFIG). The DFIG is directly supplied in the stator by the grid and in the rotor fed by the Converter Side Rotor (CSR). The initial section of the paper outlines the wind turbine system model encompassing both the electrical and mechanical components. In the subsequent section, a robust strategy of control is introduced to regulate the active and reactive (stator powers) of the machine (DFIG). The proposed control approach combines Integral Sliding Mode First Order with Artificial Neural Network control, aiming to mitigate the chattering (ripple) effect and improve the energy quality compared to conventional sliding mode first order control (SMFO). This method, denoted as Neural-SMFO-C, demonstrates improved performance and robustness in DFIG control by reducing chattering effects and enhancing energy quality. The paper concludes with the presentation and discussion of simulation results for the entire system.