An adaptive intelligent control of doubly fed wind generator for fast transient recovery

A. H.M.A. Rahim; M. A. Abido

doi:10.1109/PECon.2012.6450322

An adaptive intelligent control of doubly fed wind generator for fast transient recovery

A. H.M.A. Rahim^*, M. A. Abido

^*Corresponding author for this work

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

1 Scopus citations

Abstract

An adaptive radial basis function neural network based control has been proposed for a doubly fed induction generator connected to power system grid. The control is implemented on a capacitor energy stored device interfaced through a voltage source converter located at the generator terminal. The weights of the neural network are adapted online from the measurement of the generator speed and terminal voltage. Simulation results demonstrate that the adaptive intelligent control of the energy storage device can control the system transients effectively even under severely depressed voltage conditions. The radial basis neural network controller has the capability to learn very quickly to restore the system to normal operation smoothly.

Original language	English
Title of host publication	PECon 2012 - 2012 IEEE International Conference on Power and Energy
Pages	77-82
Number of pages	6
DOIs	https://doi.org/10.1109/PECon.2012.6450322
State	Published - 2012

Publication series

Name	PECon 2012 - 2012 IEEE International Conference on Power and Energy

Keywords

DFIG
Radial basis neural network
Wind generator
adaptive control

ASJC Scopus subject areas

Energy Engineering and Power Technology
Fuel Technology

UN SDGs

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

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10.1109/PECon.2012.6450322

Cite this

@inproceedings{ddd63908d7a6433397da857d26aeec91,

title = "An adaptive intelligent control of doubly fed wind generator for fast transient recovery",

abstract = "An adaptive radial basis function neural network based control has been proposed for a doubly fed induction generator connected to power system grid. The control is implemented on a capacitor energy stored device interfaced through a voltage source converter located at the generator terminal. The weights of the neural network are adapted online from the measurement of the generator speed and terminal voltage. Simulation results demonstrate that the adaptive intelligent control of the energy storage device can control the system transients effectively even under severely depressed voltage conditions. The radial basis neural network controller has the capability to learn very quickly to restore the system to normal operation smoothly.",

keywords = "DFIG, Radial basis neural network, Wind generator, adaptive control",

author = "Rahim, \{A. H.M.A.\} and Abido, \{M. A.\}",

year = "2012",

doi = "10.1109/PECon.2012.6450322",

language = "English",

isbn = "9781467350198",

series = "PECon 2012 - 2012 IEEE International Conference on Power and Energy",

pages = "77--82",

booktitle = "PECon 2012 - 2012 IEEE International Conference on Power and Energy",

}

An adaptive intelligent control of doubly fed wind generator for fast transient recovery. / Rahim, A. H.M.A.; Abido, M. A.
PECon 2012 - 2012 IEEE International Conference on Power and Energy. 2012. p. 77-82 6450322 (PECon 2012 - 2012 IEEE International Conference on Power and Energy).

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

TY - GEN

T1 - An adaptive intelligent control of doubly fed wind generator for fast transient recovery

AU - Rahim, A. H.M.A.

AU - Abido, M. A.

PY - 2012

Y1 - 2012

N2 - An adaptive radial basis function neural network based control has been proposed for a doubly fed induction generator connected to power system grid. The control is implemented on a capacitor energy stored device interfaced through a voltage source converter located at the generator terminal. The weights of the neural network are adapted online from the measurement of the generator speed and terminal voltage. Simulation results demonstrate that the adaptive intelligent control of the energy storage device can control the system transients effectively even under severely depressed voltage conditions. The radial basis neural network controller has the capability to learn very quickly to restore the system to normal operation smoothly.

AB - An adaptive radial basis function neural network based control has been proposed for a doubly fed induction generator connected to power system grid. The control is implemented on a capacitor energy stored device interfaced through a voltage source converter located at the generator terminal. The weights of the neural network are adapted online from the measurement of the generator speed and terminal voltage. Simulation results demonstrate that the adaptive intelligent control of the energy storage device can control the system transients effectively even under severely depressed voltage conditions. The radial basis neural network controller has the capability to learn very quickly to restore the system to normal operation smoothly.

KW - DFIG

KW - Radial basis neural network

KW - Wind generator

KW - adaptive control

UR - https://www.scopus.com/pages/publications/84874494321

U2 - 10.1109/PECon.2012.6450322

DO - 10.1109/PECon.2012.6450322

M3 - Conference contribution

AN - SCOPUS:84874494321

SN - 9781467350198

T3 - PECon 2012 - 2012 IEEE International Conference on Power and Energy

SP - 77

EP - 82

BT - PECon 2012 - 2012 IEEE International Conference on Power and Energy

ER -

An adaptive intelligent control of doubly fed wind generator for fast transient recovery

Abstract

Publication series

Keywords

ASJC Scopus subject areas

UN SDGs

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