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

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

doi:10.1049/iet-gtd.2014.0481

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

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

Electrical Engineering

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

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 language	British English
Pages (from-to)	1129-1136
Number of pages	8
Journal	IET Generation, Transmission and Distribution
Volume	9
Issue number	11
DOIs	https://doi.org/10.1049/iet-gtd.2014.0481
State	Published - 6 Aug 2015

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10.1049/iet-gtd.2014.0481

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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.",

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AU - Zahawi, Bashar

AU - Harmer, Keith

AU - Gadoue, Shady

AU - Giaouris, Damian

PY - 2015/8/6

Y1 - 2015/8/6

N2 - 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.

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

Abstract

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