Open AccessNon‐invasive identification of turbo‐generator parameters from actual transient network data
Author(s) -
Hutchison Greame,
Zahawi Bashar,
Harmer Keith,
Gadoue Shady,
Giaouris Damian
Publication year - 2015
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2014.0481
Subject(s) - turbo generator , exciter , transient (computer programming) , generator (circuit theory) , computer science , steam turbine , electric power system , identification (biology) , permanent magnet synchronous generator , control theory (sociology) , control engineering , power (physics) , engineering , voltage , electrical engineering , control (management) , artificial intelligence , mechanical engineering , physics , botany , quantum mechanics , biology , operating system
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.