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Damping enhancement of multimachine power system using adaptive generator control system with neural networks
Author(s) -
Kobayashi Takenori,
Morioka Yasuo,
Yokoyama Akihiko
Publication year - 1996
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.4391170405
Subject(s) - control theory (sociology) , electric power system , engineering , artificial neural network , control engineering , governor , adaptive control , transient (computer programming) , generator (circuit theory) , control system , power (physics) , electric generator , computer science , control (management) , artificial intelligence , physics , electrical engineering , quantum mechanics , aerospace engineering , operating system
The authors proposed a nonlinear adaptive generator control system with neutral networks for improving damping of power systems, and showed its effectiveness in a one‐machine infinite bus test power system in a previous paper. The proposed neurocontrol system adaptively generates appropriate supplementary control signals to the conventional controllers such as the automatic voltage regulator and speed governor so as to enhance transient stability and damping of the power system. In this paper, the applicability of the proposed neurocontrol system to multimachine power systems is discussed. Digital time simulations are carried out for a 4‐machine test power system, where one or several synchronous generators is equipped with the neurocontrol system. As a result, also in the multimachine power system, the proposed adaptive neurocontrol systems improve the system damping effectively and they work adaptively against the wide changes of the operating conditions and the network configuration.