Browsing by Author "Peretti, Luca"

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    motulator: Motor Drive Simulator in Python
    (2023-09-06) Tiitinen, Lauri; Hartikainen, Hannu; Peretti, Luca; Hinkkanen, Marko; Department of Electrical Engineering and Automation; Electric Drives; Electric Drives; KTH Royal Institute of Technology
    This paper deals with the time-domain simulation of electric machine drives in Python. Python offers a full-featured numerical computing environment, comparable to leading commercially available alternatives, whilst being fully open-source. Adopting the open-source ecosystem allows electric drives researchers to utilize new methods from other fields, easily share their findings, and improve collaboration with other researchers both in academia and industry. For this purpose, we have launched the Python-based time-domain simulation platform motulator for electric drives. To learn more, the reader is encouraged to visit the GitHub page of the project: https://github.com/Aalto-Electric-Drives/motulator.
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    Standstill identification of an induction motor model including deep-bar and saturation characteristics
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-09) Mölsä, Eemeli; Tiitinen, Lauri; Saarakkala, Seppo E.; Peretti, Luca; Hinkkanen, Marko; Department of Electrical Engineering and Automation; Electric Drives; KTH Royal Institute of Technology; Sulzer Pumps Finland Oy
    This article deals with standstill identification of an induction motor drive for sensorless self-commissioning purposes. The proposed identification method is based on an advanced model of a squirrel-cage induction motor. The model includes the deep-bar effect and the magnetic saturation characteristics. The excitation signals are fed to the stator using a standard inverter without compensating for its nonlinearities. The saturable stator inductance is first identified by means of a robust flux-integration test, where unknown voltage disturbances are canceled with suitably selected current pulses. Then, the deep-bar characteristics are identified by means of a dc-biased sinusoidal excitation using different frequencies. Finally, the cross-saturation characteristics of the rotor leakage inductance are identified by altering the dc bias of the excitation signal. The identified characteristics are transformed to the parameters of the advanced motor model taking into account the interrelations of the aforementioned phenomena. Since the physical phenomena affecting the standstill identification process are properly included in the identified model, fewer approximations are needed and more accurate parameter estimates are obtained. The identification procedure is validated by means of experiments using two different induction motors (5.6 and 45 kW).
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    Standstill self-commissioning of an induction motor drive
    (IEEE, 2020-10-11) Mölsä, Eemeli; Tiitinen, Lauri; Saarakkala, Seppo; Peretti, Luca; Hinkkanen, Marko; Department of Electrical Engineering and Automation; Electric Drives; KTH Royal Institute of Technology
    This paper deals with the parameter identification of an induction motor at standstill. A comprehensive identification procedure is analyzed, describing a robust flux-integration method for main-flux saturation characteristics and transient tests for rotor-side parameters. The influence of the main-flux saturation on the transient test results and on the identified rotor-side parameters is studied, and improvements are suggested. The identification procedure is validated by means of experiments using 2.2-kW and 5.6-kW induction motors.