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

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

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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 languageBritish English
Pages (from-to)1129-1136
Number of pages8
JournalIET Generation, Transmission and Distribution
Issue number11
StatePublished - 6 Aug 2015


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