A hammerstein-type fourier neural network-based identification with application to transformer vibration system modelling

Z. Jing, H. Hai, Jie Jie

Research output: Chapter in Book/Conference paperConference paper

1 Citation (Scopus)

Abstract

© 2015 IEEE. This paper aims to construct an appropriate model to represent the nonlinear transformer vibration system, with electric applies as the system inputs and the vibration response on the transformer tank as the outputs. A single-input and single-output (SISO) Hammerstein-type model is developed for identifying the nonlinear transformer vibration system, when the observed vibration on the transformer tank is derived into two components contributed by the individual vibration sources. The nonlinear system is identified by the Fourier neural network, which consists of a nonlinear element and a linear dynamic block. The order determination method based on the Lipschitz criterion as well as the back-propagation algorithm for weights update are both presented. The Hammerstein-type Fourier neural networkbased model is tested on a transformer, giving promising results for prediction of the transformer vibration.
Original languageEnglish
Title of host publicationProceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015
Place of PublicationUSA
PublisherWiley-IEEE Press
Pages1974-1979
ISBN (Print)9781467373173
DOIs
Publication statusPublished - 2015
Event10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015 - Auckland, New Zealand
Duration: 15 Jun 201517 Jun 2015

Conference

Conference10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015
CountryNew Zealand
CityAuckland
Period15/06/1517/06/15

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    Jing, Z., Hai, H., & Jie, J. (2015). A hammerstein-type fourier neural network-based identification with application to transformer vibration system modelling. In Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015 (pp. 1974-1979). Wiley-IEEE Press. https://doi.org/10.1109/ICIEA.2015.7334436