Neural-net-based nonlinear control for prevention of voltage collapse

D. Chen; R. Mohler; S. A. Shahrestani; D. J. Hill

Neural-net-based nonlinear control for prevention of voltage collapse

D. Chen
, R. Mohler
, S. A. Shahrestani
, D. J. Hill

Siemens Power Systems Control

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

8 Citations (Scopus)

Abstract

In this paper, power system stability is addressed which includes the regular, generator-angle, transient stability and load-driven voltage instability. Flexible ac transmission systems (FACTS) devices are utilized for improvement of power transfer capability as well as enhancement of power system stability. Techniques are developed for synthesis of nonlinear neural controllers to deal with the situation where generator dynamics and load dynamics are interlinked so that voltage instability or even voltage collapse is more likely to occur. The simulations illustrates the performance of the synthesized neural controllers. The techniques developed can be readily generalized to more general nonlinear power systems.

Original language	English
Pages (from-to)	2156-2161
Number of pages	6
Journal	Proceedings of the IEEE Conference on Decision and Control
Volume	3
Publication status	Published - 1999
Externally published	Yes
Event	The 38th IEEE Conference on Decision and Control (CDC) - Phoenix, AZ, USA Duration: 7 Dec 1999 → 10 Dec 1999

UN SDGs

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

SDG 7 Affordable and Clean Energy

Cite this

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title = "Neural-net-based nonlinear control for prevention of voltage collapse",

abstract = "In this paper, power system stability is addressed which includes the regular, generator-angle, transient stability and load-driven voltage instability. Flexible ac transmission systems (FACTS) devices are utilized for improvement of power transfer capability as well as enhancement of power system stability. Techniques are developed for synthesis of nonlinear neural controllers to deal with the situation where generator dynamics and load dynamics are interlinked so that voltage instability or even voltage collapse is more likely to occur. The simulations illustrates the performance of the synthesized neural controllers. The techniques developed can be readily generalized to more general nonlinear power systems.",

author = "D. Chen and R. Mohler and Shahrestani, \{S. A.\} and Hill, \{D. J.\}",

year = "1999",

language = "English",

volume = "3",

pages = "2156--2161",

journal = "Proceedings of the IEEE Conference on Decision and Control",

issn = "0191-2216",

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

note = "The 38th IEEE Conference on Decision and Control (CDC) ; Conference date: 07-12-1999 Through 10-12-1999",

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TY - JOUR

T1 - Neural-net-based nonlinear control for prevention of voltage collapse

AU - Chen, D.

AU - Mohler, R.

AU - Shahrestani, S. A.

AU - Hill, D. J.

PY - 1999

Y1 - 1999

N2 - In this paper, power system stability is addressed which includes the regular, generator-angle, transient stability and load-driven voltage instability. Flexible ac transmission systems (FACTS) devices are utilized for improvement of power transfer capability as well as enhancement of power system stability. Techniques are developed for synthesis of nonlinear neural controllers to deal with the situation where generator dynamics and load dynamics are interlinked so that voltage instability or even voltage collapse is more likely to occur. The simulations illustrates the performance of the synthesized neural controllers. The techniques developed can be readily generalized to more general nonlinear power systems.

AB - In this paper, power system stability is addressed which includes the regular, generator-angle, transient stability and load-driven voltage instability. Flexible ac transmission systems (FACTS) devices are utilized for improvement of power transfer capability as well as enhancement of power system stability. Techniques are developed for synthesis of nonlinear neural controllers to deal with the situation where generator dynamics and load dynamics are interlinked so that voltage instability or even voltage collapse is more likely to occur. The simulations illustrates the performance of the synthesized neural controllers. The techniques developed can be readily generalized to more general nonlinear power systems.

UR - https://www.scopus.com/pages/publications/0033314676

M3 - Conference article

AN - SCOPUS:0033314676

SN - 0191-2216

VL - 3

SP - 2156

EP - 2161

JO - Proceedings of the IEEE Conference on Decision and Control

JF - Proceedings of the IEEE Conference on Decision and Control

T2 - The 38th IEEE Conference on Decision and Control (CDC)

Y2 - 7 December 1999 through 10 December 1999

ER -

Neural-net-based nonlinear control for prevention of voltage collapse

Abstract

UN SDGs

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