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Synchronous machine parameter identification using particle swarm optimization

Lookup NU author(s): Graeme Hutchison, Dr Bashar Zahawi, Professor Damian Giaouris


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Synchronous machines are the most widely used machines in power generation. Identifying their parameters in a non invasive way is very challenging due to the inherent nonlinearity of machine performance. This paper proposes a synchronous machine parameter identification method using particle swarm optimization (PSO) with a constriction factor. The PSO allows a synchronous machine model output to be used as the objective function to give a new, more efficient method of parameter identification. This paper highlights the effectiveness of the proposed method for the identification of synchronous machine model parameters, using both simulation and manufacturers measured experimental data. The paper will also consider the effectiveness of the method as the number of parameters to be identified is increased.

Publication metadata

Author(s): Hutchison G, Zahawi B, Harmer K, Stedall B, Giaouris D

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 5th IET International Conference on Power Electronics, Machines and Drives (PEMD)

Year of Conference: 2010

Pages: 4pp

Publisher: IEEE


DOI: 10.1049/cp.2010.0061

Library holdings: Search Newcastle University Library for this item

ISBN: 9781849192316