Dynamic induction machine models including magnetic saturation and iron losses

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Volume Title
School of Electrical Engineering | Doctoral thesis (article-based) | Defence date: 2013-11-29
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Date
2013
Major/Subject
Mcode
Degree programme
Language
en
Pages
59 + app. 84
Series
Aalto University publication series DOCTORAL DISSERTATIONS, 171/2013
Abstract
Dynamic induction machine models are used as the basis for the design and implementation of control algorithms. Costs can be reduced by applying speed-sensorless control, and advanced control strategies open up for the possibility of using an induction machine in demanding applications. However, a reliable and good control performance requires more detailed induction machine models. This thesis deals with models including the magnetic saturation and iron losses. A small-signal model, which includes the saturation due to variations in the main flux magnitude and the load torque, is used to analyze the transient behavior of the machine. Due to the magnetic saturation, the inductances vary as a function of the operating point, and the machine appears to be salient in transients. Based on the model, an identification method for the leakage inductance is proposed. The identification is based on signal injection and can be performed as the machine is running under different load conditions. A model for the skin effect of the rotor bars can be used in combination with the leakage inductance identification in the case of an induction machine equipped with deep rotor bars. The magnetizing curve can be modeled using a simple power function. An adaptive identification method is developed for the identification of magnetizing curve parameters. Identification of the leakage inductance prior to the magnetizing curve identification improves the results in case a no-load condition cannot be reached. The stator hysteresis and eddy current losses are modeled using a nonlinear resistance. The resistance is not dependent on any frequency, and is thus defined also during transients. The resistance model is experimentally investigated both for the case of an induction machine and a nonlinear inductor. The iron loss model is used in a loss-minimizing control algorithm for the induction machine.

Dynamiska modeller av induktionsmotorn används som grund for att utforma och tillämpa styralgoritmer. Kostnadseffektiva lösningar kan uppnås genom att tillämpa styrsystem utan varvtalsmatare, dessutom har avancerade styrsystem möjliggjort att induktionsmotorn aven används i mer kravande användningsområden. For att åstadkomma bra prestanda behovs emellertid mer detaljerade modeller av induktionsmotorn. Denna avhandling handlar om modeller som inkluderar magnetisk mättnad och kärnförluster. Maskinens transienta egenskaper analyseras med hjälp av en småsignalmodell som inkluderar magnetisk mättnad orsakad av variationer i det magnetiska flödet och vridmomentet. Induktanserna i modellen varierar som funktion av arbetspunkten p.g.a. den magnetiska mättnaden, och maskinen förefaller ha utpräglade poler vid transienta förlopp. En identifieringsmetod for lackinduktansen föreslås på basen av modellen. Identifieringsmetoden baseras på injicerade signaler och kan utföras under normal användning av maskinen och vid olika grad av belastning. En modell for skineffekten i rotorn kan användas i kombination med identifieringsmetoden for maskiner som har djupa rotorspar. Magnetiseringskurvan kan beskrivas med en enkel potensfunktion. En adaptiv identifieringsmetod utvecklas för att identifiera magnetiseringskurvans parametrar. Om identifieringen inte kan utföras utan belastning, förbättras resultatet genom att identifiera lackinduktansen före magnetiseringskurvan. Hysteres- och virvelströmsförluster i statorn modelleras genom att använda en icke-linjär resistans. Frekvensen ingår ej som parameter i resistansfunktionen och resistansen ar därmed definerbar ocksa i transienta förlopp. Modellen for kärnförluster undersöks experimentellt både for en induktionsmotor och en icke-linjär induktor. Modellen implementeras aven i en algoritm for minimering av förlusterna i en induktionsmotor.
Description
Supervising professor
Luomi, Jorma, Prof., Aalto University, Department of Electrical Engineering, Finland
Hinkkanen, Marko, Prof., Aalto University, Department of Electrical Engineering, Finland
Thesis advisor
Hinkkanen, Marko, Prof., Aalto University, Department of Electrical Engineering, Finland
Keywords
induction machines, dynamic models, magnetic saturation, iron losses, induktionsmotor, dynamiska modeller, magnetisk mättnad, kärnförluster
Other note
Parts
  • [Publication 1]: M. Ranta, M. Hinkkanen, A.-K. Repo, and J. Luomi. Small-signal analysis of a saturated induction motor. In Nordic Workshop on Power and Industrial Electronics (NORPIE) 2008, Espoo, Finland, June 2008.
  • [Publication 2]: M. Ranta, M. Hinkkanen, and J. Luomi. Inductance identification of an induction machine taking load-dependent saturation into account. In International Conference on Electrical Machines (ICEM) 2008, Vilamoura, Portugal, September 2008.
  • [Publication 3]: M. Ranta, M. Hinkkanen, E. Dlala, A.-K. Repo, and J. Luomi. Inclusion of hysteresis and eddy current losses in dynamic induction machine models. In IEEE International Electric Machines & Drives Conference (IEMDC) 2009, Miami, Florida, May 2009.
  • [Publication 4]: M. Ranta, M. Hinkkanen, and J. Luomi. Rotor parameter identification of saturated induction machines. In IEEE Energy Conversion Congress and Exposition (ECCE) 2009, San Jose, California, September 2009.
  • [Publication 5]: M. Hinkkanen, A.-K. Repo, M. Ranta, and J. Luomi. Small-signal modeling of mutual saturation in induction machines. IEEE Transactions on Industry Applications, vol. 46, issue 3, pp. 965–973, May-June 2010.
  • [Publication 6]: M. Ranta, M. Hinkkanen, A. Belahcen, and J. Luomi. Inclusion of hysteresis and eddy current losses in nonlinear time-domain inductance models. In 37th Annual Conference of the IEEE Industrial Electronics Society (IECON) 2011, Melbourne, Australia, November 2011.
  • [Publication 7]: Z. Qu, M. Ranta, M. Hinkkanen, and J. Luomi. Loss-minimizing flux level control of induction motor drives. IEEE Transactions on Industry Applications, vol. 48, issue 3, pp. 952–961, May-June 2012.
  • [Publication 8]: M. Ranta and M. Hinkkanen. Online identification of parameters defining the saturation characteristics of induction machines. IEEE Transactions on Industry Applications, vol. 49, issue 5, pp. 2136–2145, Sept.- Oct. 2013.
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