Blades Pitch Angle Control and Crowbar Protection of a Wind Turbine using Adaptive Neuro-Fuzzy Inference System at Severe Faulty Conditions

Due to their superior efficiency, stability, and ability to produce maximum power at various typical operating situations, wind turbines driving doubly fed induction generator systems are widely utilized in the extraction of wind energy. These systems have stability issues, particularly at severe faulty conditions. This paper focuses on the fault ride-through of a doubly fed induction generator (DFIG) driven by a wind turbine using Adaptive Neuro-Fuzzy Inference System (ANFIS). The measured voltages and currents at the generator's terminals are used by the proposed ANFIS technique to identify the faulty conditions. ANFIS technology activates the wind turbine's modifying the wind turbine's aerodynamic torque. Additionally, the ANFIS controller turns on the crowbar resistance to protect the system's electrical components, particularly the power electronics converters and DC bus voltage. This paper compares the behavior of the DFIG under faulty conditions with and without the application of the suggested ANFIS approach. Using the Matlab TM /Simulink environment, the system is modeled and simulated. The results are then recorded and analyzed.