Title:	Bayesian optimization FCS-MPC parameters for reduction of induction motor electromagnetic noise
Authors:	Kroneisl, Michal Šmídl, Václav
Citation:	KRONEISL, M. ŠMÍDL, V. Bayesian optimization FCS-MPC parameters for reduction of induction motor electromagnetic noise. In: 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE) : /proceedings/. Piscataway: IEEE, 2020. s. 271-276. ISBN 978-1-72815-635-4, ISSN 2163-5145.
Issue Date:	2020
Publisher:	IEEE
Document type:	konferenční příspěvek conferenceObject
URI:	2-s2.0-85089470808 http://hdl.handle.net/11025/42816
ISBN:	978-1-72815-635-4
ISSN:	2163-5145
Keywords in different language:	electric drives;induction motors;industrial electronics;model predictive control;predictive control systems;tuning;variable speed drives
Abstract in different language:	Acoustic noise is an issue associated with operation of electric drives. Using an inverter in variable speed drives usually increases the acoustic noise. The problem has been recently addressed using finite control set model predictive control, where the acoustic model of the drive was obtained by identification. The controller is a compromise between current tracking and noise suppression, which is governed by tuning parameters. Finding proper tuning parameters is typically a laborious task. In this work, we propose to use Bayesian optimization to tune weights of the acoustic model in the full algorithm. The results are illustrated experimentally on induction motor of rated power of 11kW.
Rights:	Plný text je přístupný v rámci univerzity přihlášeným uživatelům. © IEEE
Appears in Collections:	Konferenční příspěvky / Conference papers (RICE) Konferenční příspěvky / Conference Papers (KEV) OBD

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