Blades Pitch Angle Control and Crowbar Protection of a Wind Turbine using Adaptive Neuro-Fuzzy Inference System at Severe Faulty Conditions
Samir, A., Bahgat, M., M. Abdelghany, A., Elzoghby, M. (2022). Blades Pitch Angle Control and Crowbar Protection of a Wind Turbine using Adaptive Neuro-Fuzzy Inference System at Severe Faulty Conditions. EKB Journal Management System, 3(3), 18-34. doi: 10.21608/ijaebs.2022.155671.1030
Ayman Safwat Samir; Mohiy Bahgat; Abdelghany M. Abdelghany; Mohammed M Elzoghby. "Blades Pitch Angle Control and Crowbar Protection of a Wind Turbine using Adaptive Neuro-Fuzzy Inference System at Severe Faulty Conditions". EKB Journal Management System, 3, 3, 2022, 18-34. doi: 10.21608/ijaebs.2022.155671.1030
Samir, A., Bahgat, M., M. Abdelghany, A., Elzoghby, M. (2022). 'Blades Pitch Angle Control and Crowbar Protection of a Wind Turbine using Adaptive Neuro-Fuzzy Inference System at Severe Faulty Conditions', EKB Journal Management System, 3(3), pp. 18-34. doi: 10.21608/ijaebs.2022.155671.1030
Samir, A., Bahgat, M., M. Abdelghany, A., Elzoghby, M. Blades Pitch Angle Control and Crowbar Protection of a Wind Turbine using Adaptive Neuro-Fuzzy Inference System at Severe Faulty Conditions. EKB Journal Management System, 2022; 3(3): 18-34. doi: 10.21608/ijaebs.2022.155671.1030

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

International Journal of Advanced Engineering and Business Sciences
Volume 3, Issue 3, October 2022, Page 18-34  XML PDF (1.28 MB)
Document Type: Original Article
DOI: 10.21608/ijaebs.2022.155671.1030
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Authors
Ayman Safwat Samir email 1; Mohiy Bahgatorcid 2; Abdelghany M. Abdelghany3; Mohammed M Elzoghby4
1Department of Electric power and machines, Faculty of Engineering, Helwan University, Cairo, Egypt.
2Department of Electric power and machines, Faculty of Engineering, Helwan University, Cairo, Egypt
3Higher Engineering Institute, Thebes Academy, Helwan University, Cairo, Egypt
4Power Dept. , Faculty of Engineering , Helwan University
Abstract
Because of 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. 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 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. Wind turbine's pitch angle controller protects the rotating parts of the system from over speeding when the fault occurs to ensure that the generator does not deviate from stability by adjusting the aerodynamic torque of the wind turbine. Crowbar resistance protects electrical parts of the system, especially DC bus voltage and power electronics converters. 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. The proposed ANFIS control system has proven its effectiveness in protecting the DFIG in the event of a grid fault.
Keywords
Wind energy; DFIG; ANFIS; Crowbar protection; pitch angle control
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