Non-invasive identification of turbogenerator parameters from actual transient network data

Greame Hutchison*, Bashar Zahawi, Keith Harmer, Shady Gadoue, Damian Giaouris

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Synchronous machines are the most widely used form of generators in electrical power systems. Identifying the parameters of these generators in a non-invasive way is very challenging because of the inherent non-linearity of power station performance. This study proposes a parameter identification method using a stochastic optimisation algorithm that is capable of identifying generator, exciter and turbine parameters using actual network data. An eighth order generator/turbine model is used in conjunction with the measured data to develop the objective function for optimisation. The effectiveness of the proposed method for the identification of turbo-generator parameters is demonstrated using data from a recorded network transient on a 178 MVA steam turbine generator connected to the UK's national grid.

Original languageEnglish
Pages (from-to)1129-1136
Number of pages8
JournalIET Generation, Transmission and Distribution
Volume9
Issue number11
Early online date27 Feb 2015
DOIs
Publication statusPublished - 6 Aug 2015

Bibliographical note

This paper is a postprint of a paper submitted to and accepted for publication in IET Generation, Transmission & Distribution and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library

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