Abstract
This paper presents a neural network (NN) 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 controllers can be guaranteed. NNs 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 (UUB). Simulation results with a 3-machine power system demonstrate the effectiveness of the proposed controller design.
Recommended Citation
W. Liu et al., "Neural Network Based Decentralized Excitation Control of Large Scale Power Systems," Proceedings of the International Joint Conference on Neural Networks, 2006. IJCNN'06, Institute of Electrical and Electronics Engineers (IEEE), Jul 2006.
The definitive version is available at https://doi.org/10.1109/IJCNN.2006.246943
Meeting Name
International Joint Conference on Neural Networks, 2006. IJCNN'06
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
Decentralized Control; Neural Networks; Power System Control; Large scale systems
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2006 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jul 2006
Included in
Computer Sciences Commons, Electrical and Computer Engineering Commons, Systems Engineering Commons
