Indirect adaptive neurocontrol scheme for a static compensator connected to a power system

Salman Mohagheghi; Ganesh K. Venayagamoorthy; Ronald G. Harley; Jung Wook Park; Mariesa L. Crow

doi:10.1016/S14746670(17)34539-1

Indirect adaptive neurocontrol scheme for a static compensator connected to a power system

Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley, Jung Wook Park, Mariesa L. Crow

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

An indirect adaptive neurocontrol scheme for a Static Compensator connected to a power system using two Artificial Neural Networks (ANNs) is presented in this paper. The ANNs are trained online and there is no need for offline data. The neurocontroller has a better performance in adaptively controlling the Static Compensator and damping the system transients, compared to conventional controllers. Preliminary results are provided to show the performance of the neurocontroller for large disturbances.

Original language	English
Pages (from-to)	623-628
Number of pages	6
Journal	IFAC Proceedings Volumes (IFAC-PapersOnline)
Volume	36
Issue number	20
DOIs	https://doi.org/10.1016/S14746670(17)34539-1
Publication status	Published - 2003
Event	5th IFAC Symposium on Power Plants and Power Systems Control 2003 - Seoul, Korea, Republic of Duration: 2003 Sep 15 → 2003 Sep 19

Bibliographical note

Funding Information:
Financial support by the National Science Foundation (NSF), USA under Grant No. ECS-023 1632 and from the Duke Power Company, Charlotte, North Carolina, USA, for this research is gratefully acknowledged.

Publisher Copyright:
© 2003 IFAC.

All Science Journal Classification (ASJC) codes

Control and Systems Engineering

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10.1016/S14746670(17)34539-1

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title = "Indirect adaptive neurocontrol scheme for a static compensator connected to a power system",

abstract = "An indirect adaptive neurocontrol scheme for a Static Compensator connected to a power system using two Artificial Neural Networks (ANNs) is presented in this paper. The ANNs are trained online and there is no need for offline data. The neurocontroller has a better performance in adaptively controlling the Static Compensator and damping the system transients, compared to conventional controllers. Preliminary results are provided to show the performance of the neurocontroller for large disturbances.",

author = "Salman Mohagheghi and Venayagamoorthy, {Ganesh K.} and Harley, {Ronald G.} and Park, {Jung Wook} and Crow, {Mariesa L.}",

note = "Funding Information: Financial support by the National Science Foundation (NSF), USA under Grant No. ECS-023 1632 and from the Duke Power Company, Charlotte, North Carolina, USA, for this research is gratefully acknowledged. Publisher Copyright: {\textcopyright} 2003 IFAC.; 5th IFAC Symposium on Power Plants and Power Systems Control 2003 ; Conference date: 15-09-2003 Through 19-09-2003",

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AU - Harley, Ronald G.

AU - Park, Jung Wook

AU - Crow, Mariesa L.

N1 - Funding Information: Financial support by the National Science Foundation (NSF), USA under Grant No. ECS-023 1632 and from the Duke Power Company, Charlotte, North Carolina, USA, for this research is gratefully acknowledged. Publisher Copyright: © 2003 IFAC.

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N2 - An indirect adaptive neurocontrol scheme for a Static Compensator connected to a power system using two Artificial Neural Networks (ANNs) is presented in this paper. The ANNs are trained online and there is no need for offline data. The neurocontroller has a better performance in adaptively controlling the Static Compensator and damping the system transients, compared to conventional controllers. Preliminary results are provided to show the performance of the neurocontroller for large disturbances.

AB - An indirect adaptive neurocontrol scheme for a Static Compensator connected to a power system using two Artificial Neural Networks (ANNs) is presented in this paper. The ANNs are trained online and there is no need for offline data. The neurocontroller has a better performance in adaptively controlling the Static Compensator and damping the system transients, compared to conventional controllers. Preliminary results are provided to show the performance of the neurocontroller for large disturbances.

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Indirect adaptive neurocontrol scheme for a static compensator connected to a power system

Abstract

Bibliographical note

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