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Park’s vector approach to detect broken rotor bars in saturated Induction motor

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PL
Metoda wektorowa Parka do wykrywania pękniętych prętów wirnika w nasyconym silniku indukcyjnym
Języki publikacji
EN
Abstrakty
EN
This paper deals with the problem of broken rotor bars fault diagnosis in saturated induction motor. Several techniques such as those based on vibration, axial leakage monitoring, zero-sequence component, negative sequence current, and motor current signature analysis have been used. However, these techniques do not take into account the effect of magnetic saturation. This paper presents a current Park’s Vector method for diagnosis in squirrel cage induction motor with the presence of magnetic saturation. The use of a current vector pattern trajectory and Modulus current spectrum analysis demonstrate that deformation of the Current Park’s Vector Pattern trajectory and the presence of new harmonics are indicators for predicting magnetic saturation and broken bars rotor fault. Our experimental results allow us to discriminate between the magnetic saturation in healthy and faulty squirrel cage induction motors.
PL
W artykule podjęto problematykę diagnostyki pękniętych prętów wirnika w nasyconym silniku indukcyjnym. Zastosowano kilka technik, takich jak oparte na drganiach, monitorowaniu upływu osiowego, składowej zerowej, składowej przeciwnej prądu i analizie sygnatur prądu silnika. Techniki te nie uwzględniają jednak wpływu nasycenia magnetycznego. W artykule przedstawiono aktualną metodę Parka Vector do diagnostyki silnika indukcyjnego klatkowego z obecnością nasycenia magnetycznego. Wykorzystanie trajektorii wektora prądu i analiza widma prądu modułowego pokazują, że deformacja trajektorii wzorca wektorowego Current Park i obecność nowych harmonicznych są wskaźnikami do przewidywania nasycenia magnetycznego i uszkodzenia wirnika z pękniętymi prętami. Nasze wyniki eksperymentalne pozwalają nam rozróżnić nasycenie magnetyczne w zdrowych i uszkodzonych silnikach indukcyjnych klatkowych.
Rocznik
Strony
219--223
Opis fizyczny
Bibliogr. 31 poz., rys., tab.
Twórcy
  • Department of Electrical Engineering, Mostaganem University, Laboratory of Renewable Energies and Electrical Systems Mostaganem, Algeria
  • Department of Electrical Engineering, Mostaganem University, Laboratory of Renewable Energies and Electrical Systems Mostaganem, Algeria
autor
  • Department of Electrical Engineering, Mostaganem University, Laboratory of Renewable Energies and Electrical Systems Mostaganem, Algeria
  • Maintenance and Industrial Safety Institute, University Es Senia 2 of Oran, Oran 31000, Algeria
Bibliografia
  • [1] Seong, H, I and Bon,G,G. Study of Induction Motor Inter-Turn Fault Part II: Online Model Based Fault Diagnosis Method, Energies 2022, 15, 977. DOI: 10.3390/en15030977
  • [2] Ahmed, R. Faults diagnosis in stator windings of high speed solid rotor induction motors using fuzzy neural network, International Journal of Power Electronics and Drive Systems, 2021, 12(1), pp.597-611. DOI:10.11591/ijpeds.v12.i1.pp597- 611.
  • [3] Biswarup, G, Arpan, C, Arunava, C and all, Diagnosis of Stator Winding Fault of Single-Phase Induction Motor Employing Wavelet Induced Residual-Convolutional Neural Network, IEEE International conference on power electronics, Drives and Energy System (PEDES), 16-19 Dec 2020, DOI: 10.1109/PEDES49360.2020.9379665
  • [4] Mohammed, S, J, B, Jassim, M, H. Inter-Turn Stator Winding Faults Diagnosis in Three-Phase Induction Motor Fed from Imbalanced Voltage Source, Journal of Engineering and Applied Sciences, 2019, pp.7591-7598. DOI: 10.36478/jeasci.2019.7591.7598.
  • [5] Reljic, D, Jerkan, D. Broken Bar Fault Detection in IM Operating Under No-Load Condition, Advances in Electrical and Computer Engineering 16(4):63-70, 2016. DOI: 10.4316/AECE.2016.04010
  • [6] Zolfaghari, S.; Noor, S.B.M.; Mehrjou, M.R.; Marhaban, M.H.; Mariun, N. Broken Rotor Bar Fault Detectionand Classification Using Wavelet Packet Signature Analysis Based on Fourier Transform and Multi-Layer Perceptron Neural Network. Appl. Sci. 2017, 8, 25.
  • [7] Edomwandekhoe, K and Liang, X, Advanced feature selection for broken rotor bar faults in induction motors, Proceedings of 2018 IEEE/IAS 54th Industrial and Commercial Power Systems Technical Conference (I&CPS), pp. 1-10, May 2018.
  • [8] Wang, Z, Yang, J, Li, H, Zhen , D, Xu, Y and Gu, F, Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis, Energies, vol. 12, no. 17, p. 3279, Jan. 2019.
  • [9] Nemec, M.; Ambrožiˇc, V.; Fišer, R.; Nedeljkovi ́c, D.; Drobniˇc, K. Induction Motor Broken Rotor Bar Detection Based on Rotor Flux Angle Monitoring. Energies 2019, 12, 794.
  • [10] Dias, C.G.; da Silva, L.C.; Luz Alves, W.A. A Histogram of Oriented Gradients Approach for Detecting Broken Bars in Squirrel-Cage Induction Motors. IEEE Trans. Instrum. Meas. 2020, 69, 6968–6981.
  • [11] Fernandez-Cavero, V.; Pons-Llinares, J.; Duque-Perez, O.; Morinigo-SOTELO, D. Detection of Broken Rotor Bars in Non-Linear Startups of Inverter-Fed Induction Motors. IEEE Trans. Ind. Appl. 2021, 57, 2559–2568.
  • [12] Li, H., Feng, G., Zhen, D., Gu, F., & Ball, A. D. (2021). A Normalized Frequency-Domai Energy Operator for Broken Rotor Bar Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement, 70. DOI: 10.1109/TIM.2020.3009011
  • [13] Noman, S, Lauri, K, Bilal, A, Muhammad, J, and all, Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study, Energies 2021, 14, 2001. DOI: 10.3390/en14072001
  • [14] Park, Y, S. Investigation on Electromagnetic Performance of Induction Motor with Rotor Bar Faults considering Motor Current Signals, Advances in Electrical and Computer Engineering 20(4), pp.37-40, 2020. DOI: 10.4316/AECE.2020.04005
  • [15] Ojeda-Aguirre, N, A, Garcia-Perez, A, and all. Reassigned Short Time Fourier Transform and K-means Method for Diagnosis of Broken Rotor Bar Detection in VSD-fed Induction Motors, Advances in Electrical and Computer Engineering 19, pp.61-68, 2019. DOI: 10.4316/AECE.2019.02008.
  • [16] Bessous, N., Sbaa, S. & Megherbi, A. C. Mechanical fault detection in rotating electrical machines using MCSA-FFT and MCSA-DWT techniques. Bulletin of the Polish Academy of Sciences: Technical Sciences, Vol 67(3), 571-582. DOI: 10.24425/bpasts.2019.129655
  • [17] Matic, D, Kanovic, Z. Vibration Based Broken Bar Detection in Induction Machine for Low Load Conditions, Advances in Electrical and Computer Engineering, 17 pp.63-70, 2017. DOI: 10.4316/AECE.2017.01007.
  • [18] Mustapha, M , Aymen, F , Abdullah A, A, and all, Diagnosis and Fault Detection of Rotor Bars in Squirrel Cage Induction Motors Using Combined Park’s Vector and Extended Park’s Vector Approaches, Electronics 2022, 11, 380. DOI: 10.3390/electronics11030380
  • [19] Shurong, W; Xin, Z ; Yao, X ; Yang, F ,Zixu ,R ; Fangxing, L, Extended Park's vector method in early inter-turn short circuit fault detection for the stator windings of offshore wind doublyfed induction generators, IET Generation, Transmission & Distribution, Vol 14, Issue 18, 18 September 2020, p. 3905 – 3912, DOI: 10.1049/iet-gtd.2020.0127.
  • [20] Fatima, H; Jeevan, S, and All, Inter-Turn Fault Diagnosis of Induction Motor Fed by PCC-VSI Using Park Vector Approach, 2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES) , 16-19 Dec. 2020, DOI: 10.1109/PEDES49360.2020.9379388.
  • [21] Gyftakis, K.N., Cardoso, A.J.M., Daviu, J.A.A. Introducing the Filtered Park’s and Filtered Extended Park’s Vector Approach to detect broken rotor bars in IM. Mech. Syst. Signal Proc. 2017, 93, 30–50.
  • [22] Tushar, G, V, Makarand, B, S, and Hiralal, S, M, Application of Multiple Parks Vector Approach for Detection of Multiple Faults in Induction Motors, Journal of Power Electronics, Vol. 17, No. 4, pp. 97982, July 2017. DOI: 10.6113/JPE.2017.17.4.972
  • [23] Izzet Yilmaz, O, N, Mohamed, B. Induction motor bearing failure detection and diagnosis: Park and Concordia Transform Approaches Comparative study, IEEE/ASME Trans. Mechatronics, Vol.13, No.2,pp.257-262, April 2008.
  • [24] Marques Cardoso, A.J. Inter-turn stator winding fault diagnosis in three-phase induction motors, by Park's vector approach, IEEE Trans. Energy Conversion, Volume: 14, pp. 595 - 598, Sep 1999.
  • [25] Nejjari, H, Benbouzid, M.H. Monitoring and diagnosis of induction motor faults using current parks vector approach, IEEE trans. Ind.Applicat.,,vol.36,no.3,May/June 2000, pp.730 – 735.
  • [26] Cruz, S, M, A. Stator winding fault diagnosis in three-phase synchronous and asynchronous motors, by the extended Park's vector approach, IEEE conf. Ind. Applicat., vol.1, pp. 395 – 401, 2000
  • [27] Jasmin, P, Saranya, R, Indragandhi, V, and all, 2D Finite Element Analysis of Asynchronous Machine Influenced Under Power Quality Perturbations, Computers, Materials & Continua 2022, vol.70, no.3, DOI:10.32604/cmc.2022.020093.
  • [28] Arkadiusz, D, and Piotr, D, Induction Motor Fault Diagnosis Based on Zero-Sequence Current Analysis, Energies 2020, 13, 6528, DOI: 10.3390/en13246528.
  • [29] Mehrdad, G, Om-Kolsoom, S, Hadi, T, An extended winding function model for induction machine modelling considering saturation effect, IET Electr. Power Appl. 2021, 15, 79–91, DOI:10.1049/elp2.12006.
  • [30] Chaouch, A. Bendiabdellah, P. Remus, R.Romary , J.P. Lecointe, “Mixed Eccentricity Fault Diagnosis in Saturated Squirrel Cage Induction Motor using Instantaneous Power Spectrum Analysis” ,PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 92, NR 7/2016,
  • [31] Chaouch, A. et A.Bendiabdellah, " Mixed Eccentricity Fault Diagnosis in Saturated Squirrel Cage Induction Motor", International Review on Modelling and Simulations (IREMOS), vol. 5,No.3, June 2012.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4d15d6c9-831b-44cf-9d22-2625453b7c24
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