Robust neural network scheme for generator side converter of doubly fed induction generator

Babar Azeem; Z. Ullah; F. Rehman; C. A. Mehmood; S. M. Ali; B. Khan; I. Hussain; K. Zeb; S. Azeem; A. Haider

doi:10.1109/RAEE.2017.8246149

Robust neural network scheme for generator side converter of doubly fed induction generator

Babar Azeem, Z. Ullah, F. Rehman, C. A. Mehmood, S. M. Ali, B. Khan, I. Hussain, K. Zeb, S. Azeem, A. Haider

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

Currently, variable speed wind turbine (WT) generators are extensively used in wind energy conversion system (WECS). Variable speed constant frequency (VSCF) generators assist in better exploitation of wind energy and enhancement of WECS efficiency. However, control action is required to provide stable and reliable operation of variable speed WECS. This paper emphasizes on doubly fed induction generator (DFIG) based WECS. Therefore, a robust neural network (RNN) is proposed for DFIG to access a direct power control scheme for generator side converter (GSC), compared with classical vector control (VC). The DFIG d-q modeling is performed in MATLAB/Simulink using synchronous reference frame. Moreover, a field oriented control (FOC) scheme is employed to control the active and reactive power of GSC. The proposed control model is built in MATLAB and accuracy of results verify the conclusion. Finally, the results of RNN control scheme are analytically and critically compared with conventional proportional integral (PI) control scheme.

Original language	English
Title of host publication	2017 International Symposium on Recent Advances in Electrical Engineering, RAEE 2017
Editors	Muhammad Shahid Nazir, Sufi Tabassum Gul, Shahzad Nadeem
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781538622346
DOIs	https://doi.org/10.1109/RAEE.2017.8246149
State	Published - 28 Jun 2017
Externally published	Yes
Event	3rd IEEE International Symposium on Recent Advances in Electrical Engineering, RAEE 2017 - Islamabad, Pakistan Duration: 24 Oct 2017 → 26 Oct 2017

Publication series

Name	2017 International Symposium on Recent Advances in Electrical Engineering, RAEE 2017
Volume	2018-January

Conference

Conference	3rd IEEE International Symposium on Recent Advances in Electrical Engineering, RAEE 2017
Country/Territory	Pakistan
City	Islamabad
Period	24/10/17 → 26/10/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

DFIG
FOC
PI Control
RNN
d-q Modeling

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Control and Optimization
Instrumentation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/RAEE.2017.8246149

Cite this

Azeem, B., Ullah, Z., Rehman, F., Mehmood, C. A., Ali, S. M., Khan, B., Hussain, I., Zeb, K., Azeem, S., & Haider, A. (2017). Robust neural network scheme for generator side converter of doubly fed induction generator. In M. S. Nazir, S. T. Gul, & S. Nadeem (Eds.), 2017 International Symposium on Recent Advances in Electrical Engineering, RAEE 2017 (pp. 1-6). (2017 International Symposium on Recent Advances in Electrical Engineering, RAEE 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RAEE.2017.8246149

Azeem, Babar ; Ullah, Z. ; Rehman, F. et al. / Robust neural network scheme for generator side converter of doubly fed induction generator. 2017 International Symposium on Recent Advances in Electrical Engineering, RAEE 2017. editor / Muhammad Shahid Nazir ; Sufi Tabassum Gul ; Shahzad Nadeem. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-6 (2017 International Symposium on Recent Advances in Electrical Engineering, RAEE 2017).

@inproceedings{8b78684bf39d465f8b137cb965659208,

title = "Robust neural network scheme for generator side converter of doubly fed induction generator",

abstract = "Currently, variable speed wind turbine (WT) generators are extensively used in wind energy conversion system (WECS). Variable speed constant frequency (VSCF) generators assist in better exploitation of wind energy and enhancement of WECS efficiency. However, control action is required to provide stable and reliable operation of variable speed WECS. This paper emphasizes on doubly fed induction generator (DFIG) based WECS. Therefore, a robust neural network (RNN) is proposed for DFIG to access a direct power control scheme for generator side converter (GSC), compared with classical vector control (VC). The DFIG d-q modeling is performed in MATLAB/Simulink using synchronous reference frame. Moreover, a field oriented control (FOC) scheme is employed to control the active and reactive power of GSC. The proposed control model is built in MATLAB and accuracy of results verify the conclusion. Finally, the results of RNN control scheme are analytically and critically compared with conventional proportional integral (PI) control scheme.",

keywords = "DFIG, FOC, PI Control, RNN, d-q Modeling",

author = "Babar Azeem and Z. Ullah and F. Rehman and Mehmood, {C. A.} and Ali, {S. M.} and B. Khan and I. Hussain and K. Zeb and S. Azeem and A. Haider",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 3rd IEEE International Symposium on Recent Advances in Electrical Engineering, RAEE 2017 ; Conference date: 24-10-2017 Through 26-10-2017",

year = "2017",

month = jun,

day = "28",

doi = "10.1109/RAEE.2017.8246149",

language = "English",

series = "2017 International Symposium on Recent Advances in Electrical Engineering, RAEE 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--6",

editor = "Nazir, {Muhammad Shahid} and Gul, {Sufi Tabassum} and Shahzad Nadeem",

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Azeem, B, Ullah, Z, Rehman, F, Mehmood, CA, Ali, SM, Khan, B, Hussain, I, Zeb, K, Azeem, S & Haider, A 2017, Robust neural network scheme for generator side converter of doubly fed induction generator. in MS Nazir, ST Gul & S Nadeem (eds), 2017 International Symposium on Recent Advances in Electrical Engineering, RAEE 2017. 2017 International Symposium on Recent Advances in Electrical Engineering, RAEE 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 3rd IEEE International Symposium on Recent Advances in Electrical Engineering, RAEE 2017, Islamabad, Pakistan, 24/10/17. https://doi.org/10.1109/RAEE.2017.8246149

Robust neural network scheme for generator side converter of doubly fed induction generator. / Azeem, Babar; Ullah, Z.; Rehman, F. et al.
2017 International Symposium on Recent Advances in Electrical Engineering, RAEE 2017. ed. / Muhammad Shahid Nazir; Sufi Tabassum Gul; Shahzad Nadeem. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-6 (2017 International Symposium on Recent Advances in Electrical Engineering, RAEE 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Robust neural network scheme for generator side converter of doubly fed induction generator

AU - Azeem, Babar

AU - Ullah, Z.

AU - Rehman, F.

AU - Mehmood, C. A.

AU - Ali, S. M.

AU - Khan, B.

AU - Hussain, I.

AU - Zeb, K.

AU - Azeem, S.

AU - Haider, A.

PY - 2017/6/28

Y1 - 2017/6/28

N2 - Currently, variable speed wind turbine (WT) generators are extensively used in wind energy conversion system (WECS). Variable speed constant frequency (VSCF) generators assist in better exploitation of wind energy and enhancement of WECS efficiency. However, control action is required to provide stable and reliable operation of variable speed WECS. This paper emphasizes on doubly fed induction generator (DFIG) based WECS. Therefore, a robust neural network (RNN) is proposed for DFIG to access a direct power control scheme for generator side converter (GSC), compared with classical vector control (VC). The DFIG d-q modeling is performed in MATLAB/Simulink using synchronous reference frame. Moreover, a field oriented control (FOC) scheme is employed to control the active and reactive power of GSC. The proposed control model is built in MATLAB and accuracy of results verify the conclusion. Finally, the results of RNN control scheme are analytically and critically compared with conventional proportional integral (PI) control scheme.

AB - Currently, variable speed wind turbine (WT) generators are extensively used in wind energy conversion system (WECS). Variable speed constant frequency (VSCF) generators assist in better exploitation of wind energy and enhancement of WECS efficiency. However, control action is required to provide stable and reliable operation of variable speed WECS. This paper emphasizes on doubly fed induction generator (DFIG) based WECS. Therefore, a robust neural network (RNN) is proposed for DFIG to access a direct power control scheme for generator side converter (GSC), compared with classical vector control (VC). The DFIG d-q modeling is performed in MATLAB/Simulink using synchronous reference frame. Moreover, a field oriented control (FOC) scheme is employed to control the active and reactive power of GSC. The proposed control model is built in MATLAB and accuracy of results verify the conclusion. Finally, the results of RNN control scheme are analytically and critically compared with conventional proportional integral (PI) control scheme.

KW - DFIG

KW - FOC

KW - PI Control

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DO - 10.1109/RAEE.2017.8246149

M3 - Conference contribution

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T3 - 2017 International Symposium on Recent Advances in Electrical Engineering, RAEE 2017

SP - 1

EP - 6

BT - 2017 International Symposium on Recent Advances in Electrical Engineering, RAEE 2017

A2 - Nazir, Muhammad Shahid

A2 - Gul, Sufi Tabassum

A2 - Nadeem, Shahzad

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd IEEE International Symposium on Recent Advances in Electrical Engineering, RAEE 2017

Y2 - 24 October 2017 through 26 October 2017

ER -

Azeem B, Ullah Z, Rehman F, Mehmood CA, Ali SM, Khan B et al. Robust neural network scheme for generator side converter of doubly fed induction generator. In Nazir MS, Gul ST, Nadeem S, editors, 2017 International Symposium on Recent Advances in Electrical Engineering, RAEE 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-6. (2017 International Symposium on Recent Advances in Electrical Engineering, RAEE 2017). doi: 10.1109/RAEE.2017.8246149

Robust neural network scheme for generator side converter of doubly fed induction generator

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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