Abstract
Impedance characteristics of shipboard power systems provide important information for studies on system stability and integration. Existing injection based impedance measurement techniques require multiple tests on the system to obtain characteristics over wide frequency ranges. In this paper, recurrent neural networks (RNNs) are used to model the small signal dynamics of power electronic systems based on a single test in which randomized signals are injected into the system. The trained RNN is then used to extract the small-signal impedances/admittances of the system. A number of tests have been carried out in simulation to verify the effectiveness of the proposed method.
Recommended Citation
P. Xiao et al., "Impedance Identification of Integrated Power System Components Using Recurrent Neural Networks.," Proceedings of the 2007 IEEE Electric Ship Technologies Symposium, Institute of Electrical and Electronics Engineers (IEEE), May 2007.
The definitive version is available at https://doi.org/10.1109/ESTS.2007.372062
Meeting Name
2007 IEEE Electric Ship Technologies Symposium
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Naval Engineering Computing; Power Electronics; Power Engineering Computing; Recurrent Neural Nets; Ships
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2007 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 May 2007
