Abstract
The problem of detecting shorted turns faults in stator windings has been difficult. The risk of the failure or the breaking down of this machine can be circumvented provided there is a proper way to detect the shorted turns faults. From literature, there are many methods of faults detection and diagnosis of the machine, however, DC-centered periodogram has not really been applied to detect and diagnose a fault in the electrical machine. This chapter describes stator winding shorted-turn fault detection of induction machine using DC-centered periodogram. Codes to analyses the DC-centered periodogram for both induction Machine under Healthy and shorted fault conditions were written from the general algorithm of periodogram. It is observed that the abnormality showed from the stator current signals for each condition corresponds to the plots generated by the DC-centered periodogram. The results obtained are also compared with another technique (DWT-Energy) using the same data. The peak values of the shorted turn-(S) is greater than the peak of the healthy-(H) state in both techniques. Thus, with DC-periodogram method, an electrical machine can be placed under close monitor for fault detection when the peak value of the PSD of a healthy machine under operation is started deviating from 0 dB/Hz.
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References
J. Robinson, C. Whelan, N.K. Haggerty, Trends in advanced motor protection and monitoring. IEEE Trans. Ind. Appl. 40, 853–860 (2004)
O. Imoru, Detection and diagnosis of faults in electrical machines. Doctoral Dissertation, Tshwane University of Technology, South Africa, 2017
W.T. Thomson, M. Fenger, Current signature analysis to detect induction motor faults. IEEE Ind. Appl. Mag. 7, 26–34 (2001)
E.D. Mitronikas, A review on the faults of electric machines used in electric ships. Adv. Power Electron. (2013)
Z. Pinjia, D. Yi, T.G. Habetler, L. Bin, A survey of condition monitoring and protection methods for medium-voltage induction motors. IEEE Trans. Ind. Appl. 47, 34–46 (2011)
O. Imoru, A. Jimoh, Y. Hamam, Origin and manifestation of electrical machine faults-a review, in International Conference on Power Engineering and Renewable Energy (ICPERE), 2014, pp. 189–194
P.S. Bhowmik, S. Pradhan, M. Prakash, Fault diagnostic and monitoring methods of induction motor: a review. Int. J. Appl. Control Electr. Electron. Eng. (IJACEEE) 1, 1–18 (2013)
C. da Costa, M. Kashiwagi, M.H. Mathias, Rotor failure detection of induction motors by wavelet transform and Fourier transform in non-stationary condition case studies. Mech. Syst. Signal Process. 1, 15–26 (2015)
O. Imoru, M.A. Bhaskar, A.A. Jimoh, Y. Hamam, B.T. Abe, J. Tsado, Detection of stator shorted-turns faults in induction machine using dc-centered periodogram? in Proceedings of The 25th World Congress on Engineering (WCE 2017). Lecture Notes in Engineering and Computer Science, 5–7 July 2017, London, U.K., pp. 376–379
A. Küçüker, M. Bayrak, Detection of stator winding fault in induction motor using instantaneous power signature analysis. Turk. J. Electr. Eng. Comput. Sci. (The Scientific and Technological Research Council of Turkey) 23, 1263–1271 (2015)
A.H. Bonnett, G.C. Soukup, Cause and analysis of stator and rotor failures in three-phase squirrel-cage induction motors. IEEE Trans. Ind. Appl. 28, 921–937 (1992)
R. Sharifi, M. Ebrahimi, Detection of stator winding faults in induction motors using three-phase current monitoring. ISA Trans. (Elsevier) 50, 14–20 (2011)
O. Imoru, L. Mokate, A.A. Jimoh, Y. Hamam, Diagnosis of Rotor Inter-turn Fault of Electrical Machine at Speed Using Stray Flux Test Method (AFRICON, 2015), pp. 1–5
S.M. Alessio, Digital Signal Processing and Spectral Analysis for Scientists: Concepts and Applications (Springer, 2015)
M.H. Hayes, Statistical Digital Signal Processing and Modeling (Wiley, 1996)
F. Auger, P. Flandrin, Improving the readability of time-frequency and time-scale representations by the reassignment method. IEEE Trans. Signal Process. 43, 1068–1089 (1995)
S.A. Fulop, K. Fitz, Algorithms for computing the time-corrected instantaneous frequency (reassigned) spectrogram, with applications. J. Acoust. Soc. Am. 119, 360–371 (2006)
R. Kasim, A.R. Abdullah, N.A. Selamat, M.F. Baharom, N. Ahmad, Battery parameters identification analysis using periodogram. Appl. Mech. Mater. 785, 687–691 (2015)
O. Imoru, M.A. Bhaskar, A.A. Jimoh, Y. Hamam, Diagnosis of stator shorted-turn faults in induction machines using discrete wavelet transform. Afr. J. Sci. Technol. Innov. Dev. (Taylor & Francis) 9, 349–355 (2017)
Acknowledgements
The authors would like to thank Rand Water Professorial Chair (Electrical Engineering) of Tshwane University of Technology, Pretoria for financing the material required to carry out an experiment for the research. The authors would like to thank the National Research Foundation (NRF) for the financial support received for the research work.
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Appendix
Appendix
1.1 Induction Machine Parameters
1.2 Matlab Codes
f_s = 2056 ; % Number of samples/sec
load(‘I_healthy.mat’) % Load Healthy Current
I_Norm=I_1a; %Phase A Current of the healthy Machine
subplot (2, 1, 1)
periodogram (I_Norm,[],length((I_Norm),f_s, ’centered’) % DC-centered periodogram plot for healthy currents
subplot (2, 1, 2)
load(‘I_Shorted.mat’) % Load Shorted turn Current
I_Shorted=I_2a; %Phase A Current of the Machine with shorted turn fault
periodogram(I_Shorted,[],length(I_Shorted),f_s,’centered’) % DC-centered periodogram plot for Machine with shorted turn fault
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Imoru, Ọ., Bhaskar, M.A., Jimoh, A.A., Hamam, Y., Tsado, J. (2019). Detection of Winding (Shorted-Turn) Fault in Induction Machine at Incipient Stage Using DC-Centered Periodogram. In: Ao, SI., Gelman, L., Kim, H. (eds) Transactions on Engineering Technologies. WCE 2017. Springer, Singapore. https://doi.org/10.1007/978-981-13-0746-1_22
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DOI: https://doi.org/10.1007/978-981-13-0746-1_22
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