Defence of doctoral thesis in the field of Electrical Power and Energy Engineering, M.Sc.(Tech.) Eemeli Mölsä

M.Sc.(Tech.) Eemeli Mölsä will defend the thesis "Dynamic Modeling and Standstill Identification for Induction Motor Drives" on 4 February 2022 at 12 in Aalto University School of Electrical Engineering, Department of Electrical Engineering and Automation.

Opponent: Dr. Marcello Pucci, Institute for Marine Engineering, Palermo, Italy
Custos: Prof. Marko Hinkkanen, Aalto University School of Electrical Engineering, Department of Electrical Engineering and Automation

The public defense will be organized via remote technology. Follow defence: https://aalto.zoom.us/j/64569697159
Zoom Quick Guide: https://www.aalto.fi/en/services/zoom-quick-guide

Thesis available for public display at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/
Doctoral theses in the School of Electrical Engineering: https://aaltodoc.aalto.fi/handle/123456789/53

Press release:

The parameters of an induction motor drive can be accurately and reliably identified at standstill

The three-phase induction motor is the most common rotating electric machine and has the advantages of simplicity and reliability. Speed of an induction motor can only be effectively adjusted using a frequency converter. The converter-fed induction motor drive is suitable not only for traditional industrial applications but also for electric vehicles, for example.

Energy consumption of the electric applications can be reduced by using precise and efficient control, which helps to improve the energy efficiency not only of the electric drive itself, but also of the whole application. Advanced control methods are mostly based on a dynamic motor model, which has to be identified at the time of commissioning. To ensure efficient commissioning of a new electrical drive, identification should be automatic and reliable, without rotating the motor shaft. However, challenges arise from the magnetic saturation, rotor deep-bar effect and the generation of sufficiently low voltages by the inverter. Therefore, a motor model incorporating these phenomena is required for identification.

In this work, an extended dynamic motor model has been developed, based on the traditional space vector model, but including saturation models for both the main flux and the rotor slot bridge, as well as a model for the rotor-cage impedance. The model can be reliably parameterised by laboratory measurements and can be used not only for identification purposes, but also, for example, for high-accuracy time-domain simulations and for the development of control algorithms.

This dissertation proposes a standstill identification method that can be implemented with a standard inverter without a need to compensate the inverter nonlinearities. The proposed method is based on the motor model developed in this work. The method first measures the saturation curve of the main flux using DC pulses as excitation. The rotor-cage impedance and cross-saturation of the rotor leakage inductance are then be identified using a DC-biased sinusoidal excitation at different frequencies. The identified characteristics can be transformed into parameters for the extended motor model developed in this work, which can easily be used to obtain accurate values for the parameters of the conventional motor model. The developed method is very suitable to facilitate the introduction of inverter drives for motors of all sizes.

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