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The popularity of high-efficiency permanent magnet synchronous motors in drive systems has continued to grow in recent years. Therefore, also the detection of their faults is becoming a very important issue. The most common fault of this type of motor is the stator winding fault. Due to the destructive character of this failure, it is necessary to use fault diagnostic methods that facilitate damage detection in its early stages. This paper presents the effectiveness of spectral and bispectrum analysis application for the detection of stator winding faults in permanent magnet synchronous motors. The analyzed diagnostic signals are stator phase current, stator phase current envelope, and stator phase current space vector module. The proposed solution is experimentally verified during various motor operating conditions. The object of the experimental verification was a 2.5 kW permanent magnet synchronous motor, the construction of which was specially prepared to facilitate inter-turn short circuits modelling. The application of bispectrum analysis discussed so far in the literature has been limited to vibration signals and detecting mechanical damages. There are no papers in the field of motor diagnostic dealing with the bispectrum analysis for stator winding fault detection, especially based on stator phase current signal.
Rocznik
Tom
Strony
art. no. e140556
Opis fizyczny
Bibliogr. 39 poz., rys., tab.
Twórcy
autor
- Wrocław University of Science and Technology, Department of Electrical Machines, Drives and Measurements, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
autor
- Wrocław University of Science and Technology, Department of Electrical Machines, Drives and Measurements, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland
Bibliografia
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- [17] C.H. Park, J. Lee, G. Ahn, M. Youn and B.D. Youn, “Fault Detection of PMSM under Non-Stationary Conditions Based on Wavelet Transformation Combined with Distance Approach,” in Proc. 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Toulouse, France, 2019, pp. 88–93, doi: 10.1109/DEMPED. 2019.8864842.
- [18] J. Hang, J. Zhang, M. Xia, S. Ding and W. Hua, “Interturn Fault Diagnosis for Model-Predictive-Controlled-PMSM Based on Cost Function and Wavelet Transform,” IEEE Trans. Power Electron., vol. 35, no. 6, pp. 6405–6418, June 2020, doi: 10.1109/TPEL.2019.2953269.
- [19] J. Rosero, A. Garcia, J. Cusido, L. Romeral and J.A. Ortega, “Fault detection by means of Hilbert Huang Transform of the stator current in a PMSM with demagnetization,” in Proc. 2007 IEEE International Symposium on Intelligent Signal Process., 2007, pp. 1–6, doi: 10.1109/WISP.2007.4447631.
- [20] J. Rosero, L. Romeral, J.A. Ortega and E. Rosero, “Short circuit fault detection in PMSM by means of empirical mode decomposition (EMD) and wigner ville distribution (WVD),” in Proc. 2008 Twenty-Third Annual IEEE Applied Power Electronics Conference and Exposition, 2008, pp. 98–103, doi: 10.1109/APEC.2008.4522706.
- [21] Z. Dogan and K. Tetik, “Diagnosis of Inter-Turn Faults Based on Fault Harmonic Component Tracking in LSPMSMsWorking Under Nonstationary Conditions,” in IEEE Access, vol. 9, pp. 92101–92112, 2021, doi: 10.1109/ACCESS.2021.3092605.
- [22] J. Rosero, J. Ortega, J. Urresty, J. Cardenas and L. Romeral, “Stator Short Circuits Detection in PMSM by means of Higher Order Spectral Analysis (HOSA),” 2009 Twenty-Fourth Annual IEEE Applied Power Electronics Conference and Exposition, 2009, pp. 964–969, doi: 10.1109/APEC.2009.4802779.
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- [24] I. Zamudio-Ramírez et al., “Automatic Diagnosis of Electromechanical Faults in Induction Motors Based on the Transient Analysis of the Stray Flux via MUSIC Methods,” in IEEE Trans. Ind. Appl., vol. 56, no. 4, pp. 3604–3613, July-Aug. 2020, doi: 10.1109/TIA.2020.2988002.
- [25] P. Ewert, “The Application of the Bispectrum Analysis to Detect the Rotor Unbalance of the Induction Motor Supplied by the Mains and Frequency Converter,” Energies, vol. 13, no. 11, p. 3009, Jun. 2020.
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- [28] P. Ewert and M. Jaworski, “Application of selected higher-order methods to detect rotor unbalance of drive system with PMSM,” 2021 IEEE 19th International Power Electronics and Motion Control Conference (PEMC), 2021, pp. 874–879, doi: 10.1109/PEMC48073. 2021.9432610.
- [29] S. Shao, R. Yan, Y. Lu, P. Wang and R.X. Gao, “DCNN-Based Multi-Signal Induction Motor Fault Diagnosis,” IEEE Trans. Instrum. Meas., vol. 69, no. 6, pp. 2658–2669, June 2020, doi: 10.1109/TIM. 2019.2925247.
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- [35] C.R.P. Courtney, S.A. Neild, P.D. Wilcox, B.W. Drinkwater, “Application of the bispectrum for detection of small nonlinearities excited sinusoidally,” in J. Sound Vib., vol. 329, no. 10, pp. 4279–4293, Oct. 2010.
- [36] M.E. Iglesias-Martínez, P. Fernández de Córdoba, J.A. Antonino-Daviu and J. Alberto Conejero, “Bispectrum Analysis of Stray Flux Signals for the Robust Detection of Winding Asymmetries in Wound Rotor Induction Motors,” 2020 IEEE Energy Conversion Congress and Exposition (ECCE), 2020, pp. 4485–4490, doi: 10.1109/ECCE44975.2020.9235360.
- [37] M. Iglesias-Martínez, J. Antonino-Daviu, P. Fernández de Córdoba, and J. Conejero, “Rotor Fault Detection in Induction Motors Based on Time-Frequency Analysis Using the Bispectrum and the Autocovariance of Stray Flux Signals,” in Energies, vol. 12, no. 4, p. 597, Feb. 2019.
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- [39] A. Paz Parra, M.C. Amaya Enciso, J. Olaya Ochoa and J.A. Palacios Peñaranda, “Stator fault diagnosis on squirrel cage induction motors by ESA and EPVA,” in Proc. 2013 Workshop on Power Electronics and Power Quality Applications (PEPQA), 2013, pp. 1–6, doi: 10.1109/PEPQA.2013.6614937.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-569af586-9324-4e15-9e33-c2c5b1d2c4a1