Non‐invasive identification of turbo‐generator parameters from actual transient network data | Zendy

Hutchison Greame | Zendy; Zahawi Bashar | Zendy; Harmer Keith | Zendy; Gadoue Shady | Zendy; Giaouris Damian | Zendy

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Non‐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 , computer science , identification (biology) , transient (computer programming) , generator (circuit theory) , turbo , engineering , electrical engineering , automotive engineering , power (physics) , 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.

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