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

Abstract

Background: Due to their superior efficiency, stability, and ability to produce maximum power under various typical operating situations, wind turbines driving doubly fed induction generator systems are frequently utilized in wind power extraction. These systems face stability problems especially at severe faulty conditions. Objective: To protect the rotating parts of the system from over speeding when the fault occurs and to ensure that the generator does not deviate from stability by adjusting the aerodynamic torque of the wind turbine. In addition, to protect electrical parts of the system, especially DC bus voltage and power electronics converters. Methods: Using Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed ANFIS technique detects the faulty conditions from the measured voltages and currents at the terminals of the generator. In case of faulty cases, an ANFIS technology activates the wind turbine's pitch angle controller and the crowbar resistance. Results: A comparison between the behavior of DFIG at faulty conditions without any fault controller and with the proposed ANFIS technique is applied. When the ANFIS technique is used, the wind system's performance and response are improved. Conclusion: The proposed ANFIS control system has proven its effectiveness in protecting the DFIG in the event of a grid fault.