Electromagnetic Torque of Switched-Reluctance Motors and Its Correlation with the Magnetic System Geometry
DOI:
https://doi.org/10.14529/power200212Keywords:
switched-reluctance drive, active part, geometric dimensions, electromagnetic torque, correlation analysis, dependence, determination coefficientAbstract
A switched-reluctance machine, being the primary component of a switched-reluctance drive,
is a promising yet understudied electromechanical energy converter. Any industry can potentially expand its use of these machines. Creating a globally competitive, high-performance, and cost-effective switched-reluctance drive requires scientifically solid data on how the geometric variables of the magnetic system correlate with, and affect, the average electromagnetic torque. The goal hereof is to find how strongly this average value correlates with the geometric variables of the active machine components, and whether this correlation is positive or negative. Correlation analysis identified how strongly the structural parameters under consideration affected the average electromagnetic torque. The numerical value of the latter was calculated in MATLAB interfacing with the magnetic field calculation and visualization software FEMM 4.2. A Monte Carlo algorithm-based stochastic method was applied to find the numerical values of torque. Research effort described herein does demonstrate a correlation between the structural elements of the magnetic system and the values of the electromagnetic torque; it also shows the strength of such correlation.
References
Petrushin A.D., Shaykhiev A.R. [Reducing the Pulsation of the Electromagnetic Moment of the Traction Switched-Reluctance Drive with a Phase Overlap of 90 Electrical Degrees]. Vestnik RGUPS, 2004, no. 3, pp. 111–113. (in Russ.)
Ptakh G.K. [Switched-Reluctance Reactive Electric Drive of Medium and High Power: Foreign and Domestic Experience]. Electrical Engineering: Online Electronic Scientific Journal [Russian Internet Journal of Electrical Engineering], 2015, vol. 2, no. 3, pp. 23–33 (in Russ.) DOI: 10.24892/rijee/20150305
Korolev V.V. [Switched-Reluctance Electromechanical Converters in a Modern Car]. Mezhdunar. nauch.-tekhn. konf. AAI “Avtomobile- i traktorostroenie v Rossii: prioritety razvitiya i podgotovka kadrov” [International. Scientific-Technical Conf. AAI “Automobile and Tractor Construction in Russia: Development Priorities and Training”]. Moscow, MSTU “MAMI” Publ., 2010, vol. 3, pp. 46–54. (in Russ.)
Kolomeytsev V.L., Pakhomin S.A., Kraynov D.V., Pakhomin L.S., Prokopets A.I., Rednov F.A. [Switched-Reluctance Electric Drive of the Trolleybus]. Trudy VIII Mezhdunarodnoy (XIX Vserossiyskoy) konferentsii po avtomatizirovannomu elektroprivodu AEP-2014 [Proceedings of the VIII International (XIX All-Russian) Conference on Automated Electric Drive AEP-2014]. Saransk, 2014, vol. 2, pp. 200–205. (in Russ.)
Kocan S., Rafajdus P. Dynamic Model of High Speed Switched Reluctance Motor for Automotive Applications. 13th International Scientific Conference on Sustainable, Modern and Safe Transport (TRANSCOM 2019), Slovak Republic, 2019, vol. 40, pp. 302–309. DOI: 10.1016/j.trpro.2019.07.045
Karnaukhov N.F., Dzayan G.V., Rusin N.V. [Mathematical Model of a Mechatronic System's Switched -Jet Engine with Single-Pulse Power Supply]. Matematicheskie metody v tekhnike i tekhnologiyakh (MMTT-21): sb. tr. XXI mezhdunar. nauch. konf. [Mathematical Methods in Engineering and Technology (MMTT-21)]. Saratov, 2008, vol. 7, pp. 25–27. (in Russ.).
Shevkunova A.V. [Design of a Switched-Reluctance Motor as a Unit of a Controlled Drive System Using Optimization Algorithms]. Internet-journal Science of Science, 2016, vol. 8, no. 4. Available at: http://naukovedenie.ru/PDF/47TVN416.pdf (accessed 21.05.2020). (in Russ.)
Usman Jamil M., Kongprawechnon W., Chayopitak N. Average Torque Control of a Switched Reluctance Motor Drive for Light Electric Vehicle Applications. 20th IFAC World Congress, 2017, vol. 50, no. 1, pp. 11535–11540. DOI: 10.1016/j.ifacol.2017.08.1628
Brumercikova E., Bukova B. The Regression and Correlation Analysis of Carried Persons by Means of Public Passenger Transport of the Slovak Republic. LOGI 2019 – Horizons of Autonomous Mobility in Europe, Slovakia, 2020, vol. 44, pp. 61–68. DOI:10.1016/j.trpro.2020.02.010
Altman M. A More Scientific Approach to Applied Economics: Reconstructing Statistical, Analytical Significance, and Correlation Analysis. Economic Analysis and Policy, 2020, vol. 66, pp. 315–324. DOI: 10.1016/j.eap.2020.05.006
Neuymin V.G., Erokhin P.M., Maksimenko D.M. [Verification of the Circuit Binding Model and Identification of Gross Errors in Telemetry Data in PC “RASTRWIN3”]. Bulletin of the South Ural State University,
Ser. Power Engineering, 2016, vol. 16, no. 1, pp. 24–29. (in Russ.) DOI: 10.14529/power160104
Zhou Y., Chen D.-R. The Optimal Rate of Canonical Correlation Analysis for Stochastic Processes. Journal of Statistical Planning and Inference, 2020, vol. 207, pp. 276–287. DOI: 10.1016/j.jspi.2020.01.003
Petrushin A.D., Kashuba A.V., Shevkunova A.V. Optimizatsiya VIM [Optimization of SRM]. Computer Program, no. 2016618039, 2016.
Petrushin A.D., Kashuba A.V. Dynamic Optimization of Switched-Reluctance Motors. Russian Engineering Research, 2018, vol. 38, no. 9, pp. 705–706. DOI: 10.3103/S1068798X1809023X
Kashuba A.V. [Optimization Method for Forming Geometric Dimensions of the Tooth Zone of a Switched Reluctance motor]. Transportation Systems and Technology, 2020, vol. 5, no. 1, pp. 30–47. (in Russ.) DOI: 10.17816/transsyst20195100-00
Dufek J., Mickus I. Optimal Time Step Length and Statistics in Monte Carlo Burnup Simulations. Annals of Nuclear Energy, 2020, vol. 139. DOI: 10.1016/j.anucene.2019.107244
Tian С., Hu X. Mathematical Modeling of Security Impact Analysis of Communication Network Based on Monte Carlo Algorithm. Computer Communications, 2020, vol. 157, pp. 20–27. DOI: 10.1016/j.comcom.2020.04.005
Jentzscha Е., Gülb Ö., Öznergiz E. A Comprehensive Electric Field Analysis of a Multifunctional Electrospinning Platform. Journal of Electrostatics, 2013, vol. 71, no. 3, pp. 294–298. DOI: 10.1016/j.elstat.2012.12.007
Kashuba A.V. [Features of Computer Simulation of Electromagnetic Processes in a Switched Reluctance Motor]. Transport: nauka, obrazovanie, proizvodstvo (Transport-2017) [Transport: Science, Education, Production (Transport-2017)]. Rostov-on-Don, 2017, pp. 145–149. (in Russ.)
Hitoshi Takinami А., Borges Cruz Bruno R., Soaresde Lima L. Design, Simulation and Development of
a Magnetic Levitation System (MAGLEV). Results in Physics, 2020. DOI: 10.1016/j.rinp.2020.103115
