NDLI: Real-time neural sliding mode field oriented control for a DFIG-based wind turbine under balanced and unbalanced grid conditions

Content Provider	IET Digital Library
Author	Djilali, Larbi Sanchez, Edgar N. Belkheiri, Mohammed
Abstract	This study proposes a real-time sliding mode field oriented control for a doubly-fed induction generator (DFIG)-based wind turbine prototype connected to the grid. The proposed controller is used to track the desired direct current (DC) voltage reference at the output of the DC link, to maintain constant the grid power factor at the step-up transformer terminals controlled by the grid side converter, and to force independently the stator active and reactive power to track desired values through the rotor currents controlled by the rotor side converter. This control scheme is based on a recurrent high-order neural network (RHONN) identifier trained on-line by an extended Kalman filter. The RHONN is used to approximate the DC link and the DFIG mathematical models. The adequate approximation helps to calculate the exact equivalent control part of the sliding mode controller and to eliminate the effects of disturbances and unknown dynamics appearing in the grid, which improves the robustness of the control scheme. This controller is experimentally validated on a 1/4 HP DFIG prototype and tested for variable wind speed to track a time-varying power reference and to extract the maximum power from the wind, under both balanced and unbalanced grid conditions.
Starting Page	618
Ending Page	632
Page Count	15
ISSN	17521416
Volume Number	13
e-ISSN	17521424
Issue Number	Issue 4, Mar (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/13/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2018.5002
Journal	IET Renewable Power Generation
Publisher Date	2019-01-08
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Acceleration Angular Velocity Control Asynchronous Generator Asynchronous Machine Balanced Grid Condition Control of Electric Power System Current Control DFIG Mathematical Model DFIG-based Wind Turbine Prototype Direct Current Voltage Reference Doubly-fed Induction Generator-based Wind Turbine Prototype Electric Current Control Extended Kalman Filter Grid Disturbances Grid Side Converter Kalman Filter Learning in AI Machine Control Mathematical Analysis Multivariable Control System Neurocontrol Neurocontrollers Nonlinear Filter Power 0.25 Hp Power And Energy Control Power Converter Power Factor Power Generation Control Power Generation Fault Power Grid Power Supplies to Apparatus Power System Control Power System Economics Power System Managemen Power System Operation Power Transformer Reactive Power Control Reactive Power Tracking Real Time Neural Sliding Mode Field Oriented Control Recurrent High-order Neural Network Identifier Recurrent Neural Nets RHONN Rotation Control Rotor Side Converter Rotors Statistics Stator Stators Step-up Transformer Terminals Time-varying Power Reference Transformer And Reactors Unbalanced Grid Condition Variable Structure System Variable Wind Speed Profile Velocity Wind Power Plant Wind Turbine
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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