|Title:||Comparison of IPMSM parameter estimation methods for motor efficiency|
Hackl, Christoph M.
|Citation:||GLAC, A., ŠMÍDL, V., PEROUTKA, Z., HACKL, C.h.M. Comparison of IPMSM parameter estimation methods for motor efficiency. In: Proceedings : IECON 2020 : 46th Annual Conference of the IEEE Industrial Electronics Society. Piscataway: IEEE, 2020. s. 895-900. ISBN 978-1-72815-414-5.|
|Document type:||konferenční příspěvek|
|Keywords in different language:||recursive least squares (RLS);torque per current ratio (TPC);DC-link current;interior permanent magnet synchronous motor (IPMSM);maximum torque per ampere (MTPA);maximum torque per current (MTPC);flux linkage map;efficiency compare|
|Abstract in different language:||Efficiency of an IPMSM motor is influenced by the operating point (OP) of the machine. The optimal operating point can be found by using either direct search methods or model-based methods. Model-based methods are sensitive to parameter uncertainties of the equivalent circuit of the drive. In this paper, the impact of various parameter estimation methods on the motor efficiency is compared. Studied methods are Recursive Least Squares (RLS), frequency domain identification at standstill, and the flux linkage map method. Results are compared to direct search methods, where the efficiency is evaluated using the power consumption measurement and direct measurement of the torque. Comparison is performed on a grid of various setpoints (currents and speed). The RLS is tested in two versions of linearization of the flux: one using only the inductances, the second estimating also their offset. Each method is able to obtain good results for some OPs and bad results for other OPs. Overall, good performance was obtained for direct search, offline identified parameters and flux linkage map and RLS with flux offset. RLS without the flux offset does not yield consistent results which implies significantly lower efficiency.|
|Rights:||Plný text je přístupný v rámci univerzity přihlášeným uživatelům.|
|Appears in Collections:||Články / Articles (KEV)|
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