Abstract
A novel method for nonlinear identification of a static compensator connected to a power system using continually online trained (COT) artificial neural networks (ANNs) is presented in this paper. The identifier is successfully trained online to track the dynamics of the power network without any need for offline data and can be used in designing an adaptive neurocontroller for a static compensator connected to such system.
Recommended Citation
S. Mohagheghi et al., "An Adaptive Neural Network Identifier for Effective Control of a Static Compensator Connected to a Power System," Proceedings of the International Joint Conference on Neural Networks, 2003 (2003, Portland, OR), vol. 4, pp. 2964 - 2949, Institute of Electrical and Electronics Engineers (IEEE), Jul 2003.
The definitive version is available at https://doi.org/10.1109/IJCNN.2003.1224042
Meeting Name
International Joint Conference on Neural Networks, 2003 (2003: Jul. 20-24, Portland, OR)
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Adaptive Neural Network Identifier; Adaptive Neurocontroller; Continually Online Trained Artificial Neural Networks; Identification; Neurocontrollers; Power Electronic Based Shunt Connected Flexible AC Transmission System Devices; Power System; Power System Control; Static VAr Compensators; Static Compensator
International Standard Book Number (ISBN)
0-7803-7898-9
International Standard Serial Number (ISSN)
1098-7576
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2003 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jul 2003
