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Optimal control of maximum torque current ratio for synchronous reluctance motor based on virtual signal injection algorithm

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Języki publikacji
EN
Abstrakty
EN
This study focuses on the maximum torque current ratio control of synchronous reluctance motors and proposes an optimized control method for the maximum torque current ratio of synchronous reluctance motors based on virtual signal injection. Firstly, the research on the maximum torque current ratio control of synchronous reluctance motors based on the virtual signal injection method is conducted, and the existing virtual unipolar square wave signal injection method is analyzed and studied. Secondly, a non-parametric maximum torque current ratio control strategy based on a synchronous reluctance motor combined with the virtual signal injection method is proposed. This strategy does not involve complex parameter calculations, and the control accuracy is not limited by the accuracy of the parameters in the model. The experimental results showed that under the control of virtual bipolar and unipolar square wave signal injection methods, the load torque was converted from 2 Nm to 6 Nm at t = 2.5 s, and there was a significant change in the current amplitude and waveform of the current vector. Under the control of the bipolar injection method, the current amplitude waveform of the motor was lower than that of the unipolar waveform, and the current was smaller. After the load suddenly changed, it could enter a stable state faster. After the load changed at t = 2.5 s, the phase angle of the current vector was quickly adjusted and stabilized under the control of the bipolar signal. The designed method has a good optimization effect compared to the traditional virtual signal injection method, and can achieve high-performance maximum torque current ratio optimization control on synchronous reluctance motors.
Rocznik
Strony
451--466
Opis fizyczny
Bibliogr. 28 poz., rys., tab., wykr., wz.
Twórcy
autor
  • The Department of Electrical Engineering, Hebei Chemical and Pharmaceutical College, Shijiazhuang, 050026, China
Bibliografia
  • [1] Danjuma M.U., Yusuf B., Yusuf I., Reliability, availability, maintainability, and dependability analysis of cold standby series-parallel system, Journal of Computational and Cognitive Engineering, vol. 1, no. 4, pp. 193–200 (2022), DOI: 10.47852/bonviewJCCE2202144.
  • [2] Park J.S., Kim J.M., Barlat F., Lim J.H., Pierron F., Kim J.H., Characterization of dynamic hardening behavior at intermediate strain rates using the virtual fields method, Mechanics of Materials, vol. 162, no. 4, 104101 (2021), DOI: 10.1016/j.mechmat.2021.104101.
  • [3] Liu Y., Shu S., Wei H.Y., Yang Y., A virtual element method for the steady-state Poisson-Nernst-Planck equations on polygonal meshes, Computers and Mathematics with Applications, vol. 102, no. 15, pp. 95–112 (2021). DOI: j.camwa.2021.10.002.
  • [4] Wu C.L., Zhang L.L., Song G., Yin H.M., Inclusion-based boundary element method for virtual experiments of particulate composites containing arbitrarily shaped inhomogeneities, Engineering Analysis with Boundary Elements, vol. 135, no. 1226, pp. 93–114 (2022), DOI: 10.1016/j.enganabound.2021.10.024.
  • [5] Wu C.L., Yin H.M., The inclusion-based boundary element method (iBEM) for virtual experiments of elastic composites, Engineering Analysis with Boundary Elements, vol. 124, no. 1226, pp. 245–258 (2021), DOI: 10.1016/j.enganabound.2020.12.020.
  • [6] Antonietti P.F., Manzini G., Mazzieri I., Mourad H.M., Verani M., The arbitrary-order virtual element method for linear elastodynamics models: convergence, stability and dispersion-dissipation analysis, International Journal for Numerical Methods in Engineering, vol. 122, no. 4, pp. 934–971 (2021), DOI: 10.1002/nme.6569.
  • [7] Zhang P.Y., Wang C., Kumar N., Liu L., Space-air-ground integrated multi-domain network resource orchestration based on virtual network architecture: a DRL method, IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 2798–2808 (2022), DOI: 10.1109/TITS.2021.3099477.
  • [8] Naseer M.U., Kallaste A., Asad B., Vainmann T., Rassolkin A., Analytical modelling of synchronous reluctance motor including non-linear magnetic condition, IET Electric Power Applications, vol. 16, no. 4, pp. 511–524 (2022), DOI: 10.1049/elp2.12172.
  • [9] Qu J.Z., Jatskevich J., Zhang C.N., Zhang S., Improved multiple vector model predictive torque control of permanent magnet synchronous motor for reducing torque ripple, IET Electric Power Applications, vol. 15, no. 5, pp. 681–695 (2021), DOI: 10.1049/elp2.12050.
  • [10] Pasqualotto D., Zigliotto M., A comprehensive approach to convolutional neural networks-based condition monitoring of permanent magnet synchronous motor drives, IET Electric Power Applications, vol. 15, no. 7, pp. 947–962 (2021), DOI: 10.1049/elp2.12059.
  • [11] Yao Z., Zhao J., Song J., Dong P., He Z.Y., Zong K.F., Research on selection criterion of design tolerance for air-core permanent magnet synchronous linear motor, Transactions on Industrial Electronics, vol. 68, no. 4, pp. 3336–3347 (2021), DOI: 10.1109/TIE.2020.2979574.
  • [12] Liu X.M., Yu H.T., Gerada D., Xu Z.Y., Qiu H.B., Jia W.Y., Yang C.X., Comparison of rare-earth and hybrid-magnet mover configurations for a permanent magnet synchronous linear motor, IET Electric Power Applications, vol. 15, no. 3, pp. 321–331 (2021), DOI: 10.1049/elp2.12024.
  • [13] Zhang Z., Yin S., Wu J., Huang S., Dynamic and highly energy-efficient virtual network embedding method based on elastic optical networks, Optical Engineering, vol. 60, no. 12, pp. 2–17 (2021), DOI: 10.1117/1.OE.60.12.126106.
  • [14] Hong Y., Yoon S., Kim Y.S., Jang H., System-level virtual sensing method in building energy systems using autoencoder: under the limited sensors and operational datasets, Applied Energy, vol. 301, no. 1, pp. 2–12 (2021), DOI: 10.1016/j.apenergy.2021.117458.
  • [15] Tawfiq K.B., Ibrahim M.N., El-Kholy E., Sergeant P., Refurbishing three-phase synchronous reluctance machines to multiphase machines, Electrical Engineering, vol. 103, no. 1, pp. 139–152 (2021), DOI: 10.1007/s00202-020-01064-w.
  • [16] Parvathy M.L., Eshwar K., Thippiripati V.K., A modified duty-modulated predictive current control for permanent magnet synchronous motor drive, IET Electric Power Applications, vol. 15, no. 1, pp. 25–38 (2021), DOI: 10.1049/elp2.12004.
  • [17] Fu R., Cao Y., Hybrid flux predictor-based predictive flux control of permanent magnet synchronous motor drives, IET Electric Power Applications, vol. 16, no. 4, pp. 472–482 (2022), DOI: 10.1049/elp2.12168.
  • [18] Wang Z.Q., Xie S.F., Jin X.F., Shi T.N., Yang M.B., A novel deadbeat predictive current control of permanent magnet synchronous motor based on oversampling scheme, IET Electric Power Applications, vol. 15, no. 8, pp. 1029–1044 (2021), DOI: 10.1049/elp2.12078.
  • [19] Zhu Y., Zhao C., Yin L., Zhou H., Xing C., A comparative study of switched reluctance motors with a single hase and a novel Synchronous double hase excitation mode, IET Electric Power Applications, vol. 15, no. 9, pp. 1217–1231 (2021), DOI: 10.1049/elp2.12093.
  • [20] Lin Y., Sun Y., Wang Y., Cai S., Shen J.X., Radial electromagnetic force and vibration in synchronous reluctance motors with asymmetric rotor structures, IET Electric Power Applications, vol. 15, no. 9, pp. 1125–1137 (2021), DOI: 10.1049/elp2.12080.
  • [21] Liu X.M., Yu H.T., Gerada D., Xu Z.Y., Qin H.B., Jia W.Y., Yang C.X., Comparison of rare-earth and hybrid-magnet mover configurations for a permanent magnet synchronous linear motor, IET Electric Power Applications, vol. 15, no. 3, pp. 321–331 (2021), DOI: 10.1049/elp2.12024.
  • [22] Debnath S., Fuzzy quadripartitioned neutrosophic soft matrix theory and its decision-making approach, Journal of Computational and Cognitive Engineering, vol. 1, no. 2, pp. 88–93 (2022).
  • [23] Yousefi-Talouki A., Pescetto P., Pellegrino G., Boldea L., Combined Active Flux and High Frequency Injection Methods for Sensorless Direct Flux Vector Control of Synchronous Reluctance Machines, IEEE Transactions on Power Electronics, vol. 99, no. 1, pp. 1–2 (2017), https://ieeexplore.ieee.org/ document/7907329.
  • [24] Armando E., Bojoi R.I., Guglielmi P., Pellegrino G., Experimental Identification of the Magnetic Model of Synchronous Machines, IEEE Transactions on Industry Applications, vol. 49, no. 5, pp. 2116–2125 (2013), DOI: 10.1109/TMAG.2015.2438872.
  • [25] Varatharajan A., Pellegrino G., Sensorless Synchronous Reluctance Motor Drives: A General Adaptive Projection Vector Approach for Position Estimation, IEEE Transactions on Industry Applications, vol. 56, no. 2, pp. 1495–1504 (2020), DOI: 10.1109/TIA.2019.2961986.
  • [26] Cupertino F., Giangrande P., Pellegrino G., Salvatore L., End Effects in Linear Tubular Motors and Compensated Position Sensorless Control Based on Pulsating Voltage Injection, IEEE Transactions on Industrial Electronics, vol. 58, no. 2, pp. 494–502 (2011), DOI: 10.1109/TIE.2010.2046577.
  • [27] Cupertino F., Pellegrino G., Giangrande P., Salvatore L., Sensorless Position Control of Permanent Magnet Motors with Pulsating Current Injection and Compensation of Motor End Effects, IEEE Transactions on Industry Applications, vol. 47, no. 3, pp. 1371–1379 (2011), DOI: 10.1109/TIA.2011.2126542.
  • [28] Boazzo B., Pellegrino G., Model-Based Direct Flux Vector Control of Permanent-Magnet Synchronous Motor Drives, IEEE Transactions on Industry Applications, vol. 51, no. 4, pp. 3126–3136 (2015), DOI: 10.1109/TIA.2015.2399619.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-31bfebb4-8e71-4dbd-b8b4-6d9e5537f568
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