Classification of severity of low-voltage motor coil arcing fault using statistical techniques

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Classification of severity of low-voltage motor coil arcing fault using statistical techniques

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Published

Developments spanning a major portion of the previous decade have witnessed the emergence of high/medium/low-voltage arcing fault as one of the more prominent issues confronting the power industry and associated researchers alike. Research over the past decade has been dedicated to the modelling, detection and/or monitoring of arcing faults at various voltage levels and power system apparatuses. The performance of statistical methodologies utilised to classify the severity of low-voltage arcing in a motor coil is presented and compared. The approaches revolve around the utilisation of statistical techniques such as spectral angle mapper, spectral information divergence and linear discriminant analysis to classify the severity of the motor coil arcing fault. Dedicated test-benches are utilised to simulate the arcing phenomenon of varying severity in a motor coil within laboratory environment. Hall-effect sensors in conjunction with the data acquisition module of LabVIEW provide an able means for data collection which is then subjected to offline analysis. The conceptual approach preceding the classification process revolves around the extraction of pre-decided features associated with the current signal gathered during the arcing process. These features are associated with the higher-order harmonic content of the current signal. Comparative analysis of the higher-order harmonic content in the arcing current as obtained from the test-bench and that from contemporary mathematical models for low-voltage arcing faults is presented to validate the choice of parameters for the spectral signature. The extracted features are utilised for classifying the severity of the motor coil arcing fault using the approaches mentioned above. The performance of the classification approaches has been evaluated based on the accuracy of classification, robustness and feasibility of implementation of the approach. The classification results seem very promising in terms of accuracy and feasibility of approach for real-time implementation.

Inspec keywords: fault diagnosis; mathematical analysis; Hall effect devices; electric sensing devices; feature extraction; electric motors; arcs (electric); statistical analysis

Other keywords: mathematical model; Hall-effect sensors; linear discriminant analysis; spectral angle mapper; spectral information divergence; spectral signature; higher-order harmonic content; of low-voltage motor coil arcing fault; power industry; statistical techniques; power system apparatuses

Subjects: a.c. machines; Mathematical analysis; Sensing devices and transducers; Gaseous insulation, breakdown and discharges; Other topics in statistics; d.c. machines

References

    1. 1)
      • http://people.revoledu.com/kardi/tutorial/LDA/index.html.
    2. 2)
      • Charytoniuk, W., Lee, W.J., Chen, M.S., Cultrera, J., Maffetone, T.: `Industrial and Commercial Power Systems Technical Conf., 2000', Conference Record. Papers Presented at the 2000 Annual Meeting, 7–11 May 2000, p. 15–20, 2000, IEEE, Digital Object Identifier: 10.1109/ICPS.2000.854352.
    3. 3)
    4. 4)
      • Momoh, J.A., Ishola-Salawu, A.S.: `A new arcing fault modeling and detection technique for navy IPS power system', Power Engineering Society General Meeting, 18–22 June 2006, p. 7, 2006, IEEE, Digital Object Identifier: 10.1109/PES.2006.1709620.
    5. 5)
    6. 6)
      • Gammon, T., Mathews, J.: `Arcing-fault models for low-voltage power systems', Industrial and Commercial Power Systems Technical Conf., 2000. Conference Record. Papers Presented at the 2000 Annual Meeting, 7–11 May 2000, p. 199–126, 2000, IEEE, Digital Object Identifier: 10.1109/ICPS.2000.854363.
    7. 7)
    8. 8)
      • R.E. Lee , M.T. Bishop . Performance testing of the ratio ground relay on a four-wire distribution feeder. IEEE Trans. Power Appl. Syst. , 2943 - 2949
    9. 9)
      • Momoh, J.A., Kumar, D.M.V., Ishola-Salawu, A.S., Sowah, R., Button, R.: `Lab VIEW based implementation of remedial action for DC arcing faults in a spacecraft', Power Engineering Society General Meeting, 13–17 July 2003, 1, 2003, IEEE, Digital Object Identifier: 10.1109/PES.2003.1267226.
    10. 10)
      • T.S. Sidhu , G. Singh , M.S. Sachdev . Arcing fault detection using artificial neural networks. Neurocomput. , 225 - 241
    11. 11)
    12. 12)
      • Gammon, T., Mathews, J.: `The historical evolution of arcing-fault models for low-voltage systems', Industrial and Commercial Power Systems Technical Conf., 1999, Sparks, NV, p. 1–6.
    13. 13)
      • H. Szu , X.-Y. Yang , B. Telfer , Y. Sheng . Neural network and wavelet transform for scale-invariant data classification. Phys. Rev. E , 1497 - 1501
    14. 14)
      • T. Gammon , J. Mathews . Instantaneous arcing-fault models developed for building system analysis. IEEE Trans. Ind. Appl. , 1 , 197 - 203
    15. 15)
      • Sidhu, T.S., Singh, G., Sachdev, M.S.: `A new technique for detection and location of arcing faults in power system apparatus', IEEE Canadian Conf. Electrical and Computer Engineering, 24–28 May 1998, 1, p. 185–188, 1998, Digital Object Identifier: 10.1109/CCECE.1998.682713.
    16. 16)
    17. 17)
      • Ece, D.G., Wells, F.M.: `Analysis and detection of arcing faults in low-voltage electrical power systems', Proc. 7th Mediterranean Electrotechnical Conf., 12–14 April 1994, 3, p. 929–935, 1994, Digital Object Identifier: 10.1109/MELCON.1994.380949.
    18. 18)
      • http://people.revoledu.com/kardi/tutorial/LDA/LDA.html.
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