Identification of the nonlinear vibration system of power transformers

Zheng Jing, Huang Hai, Jie Pan, Zhang Yanni

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This paper focuses on the identification of the nonlinear vibration system of power transformers. A Hammerstein model is used to identify the system with electrical inputs and the vibration of the transformer tank as the output. The nonlinear property of the system is modelled using a Fourier neural network consisting of a nonlinear element and a linear dynamic block. The order and weights of the network are determined based on the Lipschitz criterion and the back-propagation algorithm. This system identification method is tested on several power transformers. Promising results for predicting the transformer vibration and extracting system parameters are presented and discussed.

Original languageEnglish
Article number015005
JournalMeasurement Science and Technology
Volume28
Issue number1
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes

Fingerprint

Power Transformer
Nonlinear Vibration
Power transformers
transformers
vibration
Backpropagation algorithms
Transformer
Identification (control systems)
Vibration
Hammerstein Model
Neural networks
system identification
Back-propagation Algorithm
System Identification
Lipschitz
Neural Networks
output
Output

Cite this

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Identification of the nonlinear vibration system of power transformers. / Jing, Zheng; Hai, Huang; Pan, Jie; Yanni, Zhang.

In: Measurement Science and Technology, Vol. 28, No. 1, 015005, 01.01.2017.

Research output: Contribution to journalArticle

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