Decentralized Neural Network-based Excitation Control of Large-scale Power Systems

Abstract

This paper presents a neural network based decentralized excitation controller design for large-scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem control activities are guaranteed through rigorous stability analysis. Neural networks in the controller design are used to approximate the unknown/imprecise dynamics of the local power system and the interconnections. All signals in the closed loop system are guaranteed to be uniformly ultimately bounded. To evaluate its performance, the proposed controller design is compared with conventional controllers optimized using particle swarm optimization. Simulations with a three-machine power system under different disturbances demonstrate the effectiveness of the proposed controller design.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Decentralized Control; Large-Scale System; Neural Networks; Power System Control

International Standard Serial Number (ISSN)

1598-6446

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2007 Institute of Control, Robotics and Systems (ICROS), All rights reserved.

Publication Date

01 Jan 2007

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