D.А. Istselemov - Head of Electric Drive Division of Engineering Department of CJSC NIPO; Post-graduate Student, Department of Electrical Engineering and Electromechanics, Perm National Research Polytechnical University. E-mail: novator@runbox.com E.V. Lyubimov - Ph.D. (Eng.), Professor, Department of Electrical Engineering and Electromechanics, Perm National Research Polytechnical University. E-mail: lis@pstu.ru D.A. Dadenkov - Senior Lecturer, Department of Microprocessor Automation Equipment, Perm National Research Polytechnical University. E-mail: dadenkov@mail.ru

Electric machines are tested at the factory during the production process to verify the compliance of the actual and declared characteristics. Tests include a large amount of measuring and computing works, a list of which is given in the appropriate national and international standards. The main method of processing the results of experiments in accordance with the requirements of the standards for synchronous round rotor electrical machines is graphical analytical method. The main disadvantages of the method are a lack of precision, a high probability of the impact of human error on the final results and time consuming. At the same time almost none of the standards have algorithms to automate this process. To eliminate these shortcomings the authors developed software system for automating measurements and calculations during medium and high power synchronous round rotor electric machines - testing, which allowed to acquire necessary characteristics in automatic and manual modes, and to determine testing machines - main electromagnetic parameters on the base of analytical methods. To test the software package and to evaluate its results, a testing, using a mathematical model of the real synchronous motor STD-800-2 in real-time environment at NIPXIe-8133 controller was conducted. Parameters of such HIL model changes in real-time and matches the real object. Test results shew that raw data filtering, used in the developed software allows to reduce the deviation of the obtained parameters to a minimum even if the raw data has significant noise. For example, deviation of most determined parameters is within 6% when the noise level is 30% of the useful signal.

 

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