Multilayer perceptron and radial basis function neural networks have been traditionally used for plant identification in power systems applications of neural networks. While being efficient in tracking the plant dynamics in a relatively small system, their performance degrades as the dimensions of the plant to be identified are increased, for example in supervisory level identification of a multimachine power system for wide area control purposes. Recurrent neural networks can deal with such a problem by modeling the system as a set of differential equations and with less order of complexity. Such a recurrent neural network identifier is designed and implemented for supervisory level identification of a multimachine power system with a FACTS device. Simulation results are provided to show that the neuroidentifier can track the system dynamics with sufficient accuracy.
S. Mohagheghi et al., "A Dynamic Recurrent Neural Network for Wide Area Identification of a Multimachine Power System with a FACTS Device," Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems, 2005, Institute of Electrical and Electronics Engineers (IEEE), Jan 2005.
The definitive version is available at https://doi.org/10.1109/ISAP.2005.1599266
13th International Conference on, Intelligent Systems Application to Power Systems, 2005
Electrical and Computer Engineering
Keywords and Phrases
FACTS Device; Differential Equations; Dynamic Recurrent Neural Network; Flexible AC Transmission Systems; Multimachine Power System; Power System Identification; Power System Modeling; Power System Simulation; Recurrent Neural Nets; Static Compensator; Supervisory Level Identification; Wide Area Control; Wide Area Identification
Article - Conference proceedings
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