Identification of variable frequency induction motor models from operating data

A.B. Proca, A. Keyhani
Dept. of Electr. Eng., Ohio State Univ., Columbus, OH, USA;

This paper appears in: IEEE Transactions on Energy Conversion
Publication Date: March 2002
On page(s): 24-31
Volume: 17, Issue: 1
ISSN: 0885-8969
Reference Cited: 23
CODEN: ITCNE4
Inspec Accession Number: 7193715

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Abstract:
The parameters of the induction motor model vary as operating conditions change. Accurate knowledge of these parameters and their dependency on operating conditions is critical for optimal field oriented control. This paper presents a systematic approach to modeling an induction motor considering operating conditions. All parameters are assumed to vary as a function of the operating conditions. The parameters are estimated from transient data using a constrained optimization algorithm. The parameters are mapped to the operating conditions using polynomial functions and artificial neural networks. The model is validated for both steady state and transient conditions