Abstract
In order to improve the monitoring accuracy and quality of permanent magnet synchronous motor (PMSM) temperature variation signal, and achieve the ideal effect of high-precision monitoring of PMSM temperature variation signal, model prediction is introduced, and the monitoring method of PMSM temperature variation signal based on model prediction is studied. The wireless sensor technology is used to collect the temperature signals of various parts of the motor, integrate, clean, replace and protocol the original data, establish a deep learning network model to predict the characteristics of the motor temperature variation, identify the motor temperature variation signal, combine the variation pruning and variation interval, and use the delayed reporting strategy to monitor the early warning of the motor temperature variation signal, complete the monitoring of temperature variation signal of permanent magnet synchronous motor based on model prediction. The experimental analysis results show that the recall rate and accuracy rate of the design method are above 90%, and maintain detection efficiency above 97%, the monitoring accuracy of the temperature variation signal of the permanent magnet synchronous motor is high.
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References
Pasqualotto, D., Zigliotto, M.: A comprehensive approach to convolutional neural networks-based condition monitoring of permanent magnet synchronous motor drives. IET Electr. Power Appl. 15(7), 947–962 (2021)
Tsai, M.F., Tseng, C.S., Li, N.S., et al.: Implementation of a DSP-based speed-sensorless adaptive control for permanent-magnet synchronous motor drives with uncertain parameters using linear matrix inequality approach. IET Electr. Power Appl. 16(7), 789–804 (2022)
Sahebjam, M., Sharifian, M.B.B., Feyzi, M.R., et al.: Novel methodology for direct speed control of a permanent magnet synchronous motor with sensorless operation. IET Electr. Power Appl. 15(6), 728–741 (2021)
Parvathy, M.L., Eshwar, K., Thippiripati, V.K.: A modified duty-modulated predictive current control for permanent magnet synchronous motor drive. IET Electr. Power Appl. 15(1), 25–38 (2021)
Taherzadeh, M., Hamida, M.A., Ghanes, M., et al.: Torque estimation of permanent magnet synchronous machine using improved voltage model flux estimator. IET Electr. Power Appl. 15(6), 742–753 (2021)
Vahid, Z.F., Akbar, R.: 2-D analytical on‐load electromagnetic model for double-layer slotted interior permanent magnet synchronous machines 16(3), 394–406 (2022)
Fu, R., Cao, Y.: Hybrid flux predictor-based predictive flux control of permanent magnet synchronous motor drives. IET Electr. Power Appl. 16(4), 472–482 (2022)
Qu, J., Jatskevich, J., Zhang, C., et al.: Improved multiple vector model predictive torque control of permanent magnet synchronous motor for reducing torque ripple. IET Electr. Power Appl. 15(5), 681–695 (2021)
Ahn, J.M., Lim, D.K., Jeong, G.W.: Performance analysis of the outer-rotor surface-mounted permanent magnet synchronous motor for high altitude long endurance unmanned aerial vehicle applied grain-oriented electrical steel. J. Korean Magn. Soc. 32(2), 93–99 (2022)
Lv, K., Dong, X., Zhu, C.: Research on fault-tolerant operation strategy of permanent magnet synchronous motor with common dc bus open winding phase-breaking fault. Energies 15(8), 1–12 (2022)
Yue, H., Wang, H., Wang, Y.: Adaptive fuzzy fixed‐time tracking control for permanent magnet synchronous motor. Int. J. Robust Nonlinear Control 32(5), 3078–3095 (2022)
Li, T., Ma, R., Bai, H., et al.: A compare research on third harmonic current control for five-phase permanent magnet synchronous motor. J. Northwestern Polytech. Univ. 39(4), 865–875 (2021)
Cheng, Y., Wang, Y.H., Li, C., et al.: Sliding mode control of PMSM based on Luenberger observer. Comput. Simul. 40(4), 231–235 (2023)
Larbaoui, A., Chaouch, D.E., Belabbes, B., et al.: Application of passivity-based and sliding mode control of permanent magnet synchronous motor under controlled voltage. J. Vibr. Control 28(11/12), 1267–1278 (2022)
Cui, P., Zheng, F., Zhou, X., et al.: Current harmonic suppression for permanent magnet synchronous motor based on phase compensation resonant controller. J. Vibr. Control 28(7–8), 735–744 (2022)
Bai, C., Yin, Z., Zhang, Y., et al.: Multiple-models adaptive disturbance observer-based predictive control for linear permanent-magnet synchronous motor vector drive. IEEE Trans. Power Electron. 37(8), 9596–9611 (2022)
Si, J., Feng, C., Nie, R., et al.: A novel high torque density six-phase axial-flux permanent magnet synchronous motor with 60 degrees phase-belt toroidal winding configuration. IET Electr. Power Appl. 16(1), 41–54 (2022)
Hou, X., Wang, M., You, G., et al.: Study on speed sensorless system of permanent magnet synchronous motor based on improved direct torque control. Trans. Inst. Meas. Control 44(9), 1744–1754 (2022)
Tola, O.J., Obe, E.S., Obe, C.T., et al.: Finite element analysis of dual stator winding line start permanent magnet synchronous motor. Przeglad Elektrotechniczny 98(4), 47–52 (2022)
Babaei, M., Feyzi, M., Marashi, A.N.: Extended Poincare’ model and non-linear analysis of permanent-magnet synchronous motor scalar drive system. IET Power Electron. 15(9), 855–864 (2022)
Acknowledgement
Hunan Provincial Natural Science Foundation of China (2023JJ50270, 2023JJ50267); Hunan Provincial Department of Education Youth Fund Project (21B0690); Shaoyang City Science and Technology Plan Project (2022GZ3034); Hunan Provincial Department of Science and Technology Science and Technology Plan Project (2016TP1023).
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Liu, L., Yin, J., Sun, D., Li, H., Zhu, Q. (2024). Monitoring Method of Permanent Magnet Synchronous Motor Temperature Variation Signal Based on Model Prediction. In: Yun, L., Han, J., Han, Y. (eds) Advanced Hybrid Information Processing. ADHIP 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-031-50549-2_13
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DOI: https://doi.org/10.1007/978-3-031-50549-2_13
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