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Adaptive neuro-fuzzy controller for grid voltage dip compensations of grid connected dfig-wecs

This paper presents an adaptive neuro-fuzzy controller (NFC)to deal with grid voltage dip conditions for grid-connected operation of doubly fed induction generator (DFIG)driven wind energy conversion system (WECS). Due to the partial scale power converters, wind turbines based on DFIG are very sensi...

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Main Authors: Amin, I.K., Nasir Uddin, M., Hannan, M.A., Alam, A.H.M.Z.
Format: Conference Paper
Language:English
Published: 2020
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spelling my.uniten.dspace-130412020-07-06T06:45:34Z Adaptive neuro-fuzzy controller for grid voltage dip compensations of grid connected dfig-wecs Amin, I.K. Nasir Uddin, M. Hannan, M.A. Alam, A.H.M.Z. This paper presents an adaptive neuro-fuzzy controller (NFC)to deal with grid voltage dip conditions for grid-connected operation of doubly fed induction generator (DFIG)driven wind energy conversion system (WECS). Due to the partial scale power converters, wind turbines based on DFIG are very sensitive to grid disturbances. Current saturation at the rotor side converter (RSC)and overvoltage at the dc-link are the major concerns of DFIG driven WECS during grid-voltage fluctuation. In synchronous reference frame, an oscillatory stator flux appears during voltage dip and it is difficult to suppress with conventional proportional-integral (PI)controllers considering nonlinear system dynamics. Therefore, an adaptive-network fuzzy inference system based NFC is proposed in this paper to handle the system uncertainties and minimize the effect of grid voltage fluctuations. During normal operation, the proposed controller aims to regulate the currents as demanded by the reference real and reactive power. Under voltage dip condition, the controllers adjust the positive sequence d-q axis current components both at the grid and rotor sides by supplying required reactive power to the grid. The negative sequence reference currents at rotor end actuate the demagnetization effect of minimizing the impact of voltage dips. The simulation results exhibit the proposed NFC performance through its robust control over the rotor side currents and bus voltage during both the voltage dip and normal operation. © 2019 IEEE. 2020-02-03T03:29:58Z 2020-02-03T03:29:58Z 2019 Conference Paper 10.1109/IEMDC.2019.8785362 en
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description This paper presents an adaptive neuro-fuzzy controller (NFC)to deal with grid voltage dip conditions for grid-connected operation of doubly fed induction generator (DFIG)driven wind energy conversion system (WECS). Due to the partial scale power converters, wind turbines based on DFIG are very sensitive to grid disturbances. Current saturation at the rotor side converter (RSC)and overvoltage at the dc-link are the major concerns of DFIG driven WECS during grid-voltage fluctuation. In synchronous reference frame, an oscillatory stator flux appears during voltage dip and it is difficult to suppress with conventional proportional-integral (PI)controllers considering nonlinear system dynamics. Therefore, an adaptive-network fuzzy inference system based NFC is proposed in this paper to handle the system uncertainties and minimize the effect of grid voltage fluctuations. During normal operation, the proposed controller aims to regulate the currents as demanded by the reference real and reactive power. Under voltage dip condition, the controllers adjust the positive sequence d-q axis current components both at the grid and rotor sides by supplying required reactive power to the grid. The negative sequence reference currents at rotor end actuate the demagnetization effect of minimizing the impact of voltage dips. The simulation results exhibit the proposed NFC performance through its robust control over the rotor side currents and bus voltage during both the voltage dip and normal operation. © 2019 IEEE.
format Conference Paper
author Amin, I.K.
Nasir Uddin, M.
Hannan, M.A.
Alam, A.H.M.Z.
spellingShingle Amin, I.K.
Nasir Uddin, M.
Hannan, M.A.
Alam, A.H.M.Z.
Adaptive neuro-fuzzy controller for grid voltage dip compensations of grid connected dfig-wecs
author_facet Amin, I.K.
Nasir Uddin, M.
Hannan, M.A.
Alam, A.H.M.Z.
author_sort Amin, I.K.
title Adaptive neuro-fuzzy controller for grid voltage dip compensations of grid connected dfig-wecs
title_short Adaptive neuro-fuzzy controller for grid voltage dip compensations of grid connected dfig-wecs
title_full Adaptive neuro-fuzzy controller for grid voltage dip compensations of grid connected dfig-wecs
title_fullStr Adaptive neuro-fuzzy controller for grid voltage dip compensations of grid connected dfig-wecs
title_full_unstemmed Adaptive neuro-fuzzy controller for grid voltage dip compensations of grid connected dfig-wecs
title_sort adaptive neuro-fuzzy controller for grid voltage dip compensations of grid connected dfig-wecs
publishDate 2020
_version_ 1672614201316605952
score 13.222552

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