Electrical Engineering

, Volume 100, Issue 2, pp 1059–1067 | Cite as

Identification of acoustic signals of corona discharges under different contamination levels using wavelet transform

  • Nasir A. Al-geelani
  • M. Afendi M. Piah
  • Zulkurnain Abdul-Malek
Original Paper
  • 78 Downloads

Abstract

Partial discharges (PDs) emit energy in several ways, producing electromagnetic emissions in the form of radio waves, light and heat, and acoustic emissions in the audible and ultra-sonic ranges. These emissions enable us to detect, locate, measure, and analyse PD activity in order to identify faults before the development of failures, because once present, the damage caused by PDs always increases, leading to asset losses, outages, protection-system failure, disaster, and huge energy losses. Therefore, it is of great importance to identify different types of PDs and to assess their severity. This paper investigates the acoustic emissions associated with corona discharge (CD) from different types of sources in the time domain, and based on these it is used to detect, identify, and characterize the acoustic signals due to CD activity. Which usually takes place on polluted glass insulators used in high-voltage transmission lines and hence to differentiate abnormal operating conditions from normal ones. A laboratory experiment was conducted by preparing prototypes of the discharge. This study suggests a feature extraction and classification algorithm for CD classification. A wavelet signal processing toolbox is used to recover the CD acoustic signals by eliminating the noisy portion and to reduce the dimensions of the feature input vector. The proposed model is proven to characterize the PD activity with a high degree of integrity, which is attributed to the effect of the wavelet technique. The test results show that the proposed approach is efficient and reliable.

Keywords

Acoustic Wavelet Insulator Discharge Corona discharge Partial discharge 

Notes

Acknowledgements

The authors would like to thank the Ministry of Higher Education (MOHE) Malaysia and Universiti Teknologi Malaysia (UTM) for sponsoring this work under project vote number Q.J130000.21A2.02E55. The authors would also like to express their gratitude to all members of the Institute of High Voltage and High Current, Faculty of Electrical Engineering, UTM for their cooperation.

References

  1. 1.
    Lazarevich K (2003) Partial discharge detection and localization in high voltage transformers using an optical acoustic sensor. Virginia Polytechnic Institute and State University, BlacksburgGoogle Scholar
  2. 2.
    Smith K, Perez R (2002) Locating partial discharges in a power generating system using neural networks and wavelets. In: 2002 annual report—conference on electrical insulation and dielectric phenomena, pp 458–461Google Scholar
  3. 3.
    Lemke E, Berlinjn S, Gulski E, Muhr M, Pultrum E, Strehl T, Hauschild W, Rickmann J, Rizzi G (2008) Guide for electrical partial discharge measurements in compliance to IEC 60270. Technical Brochure no 366Google Scholar
  4. 4.
    Moreno V, Gorur R (2001) Effect of long-term corona on non-ceramic outdoor insulator housing materials. IEEE Trans Dielectr Electr Insul 8:117–128CrossRefGoogle Scholar
  5. 5.
    Pinnangudi B, Gorur R, Kroese A (2002) Energy quantification of corona discharges on polymer insulators. In: 2002 annual report—conference on electrical insulation and dielectric phenomena, pp 315–318Google Scholar
  6. 6.
    Koperundevi G, Goyal M, Das S, Roy N, Sarathi R (2010) Classification of incipient discharges in transformer insulation using acoustic emission signatures. In: 2010 annual IEEE India conference, pp 1–5Google Scholar
  7. 7.
    Sarathi R, Singh PD, Danikas MG (2007) Characterization of partial discharges in transformer oil insulation under AC and DC voltage using acoustic emission technique. J Electr Eng Bratisl 58:91Google Scholar
  8. 8.
    Fugal DL (2009) Conceptual wavelets in digital signal processing: an in-depth, practical approach for the non-mathematician. Space & Signals Technical PubGoogle Scholar
  9. 9.
    Graps A (1995) An introduction to wavelets. IEEE Comput Sci Eng 2:50–61CrossRefGoogle Scholar
  10. 10.
    Chui CK (1992) An introduction to wavelets, vol 1. Academic Press, New YorkCrossRefMATHGoogle Scholar
  11. 11.
    Chandrasekar S, Kalaivanan C, Cavallini A, Montanari GC (2009) Investigations on leakage current and phase angle characteristics of porcelain and polymeric insulator under contaminated conditions. IEEE Trans Dielectr Electr Insul 16(2):574–583CrossRefGoogle Scholar
  12. 12.
    Pylarinos D, Siderakis K, Pyrgioti E, Thalassinakis E, Vitellas I (2011) Impact of noise related waveforms on long term field leakage current measurements. IEEE Trans Dielectr Electr Insul 18(1):122–129CrossRefGoogle Scholar
  13. 13.
    Daubechies I (1992) Ten lectures on wavelets, vol 61. SIAM, PhiladelphiaCrossRefMATHGoogle Scholar
  14. 14.
    Reid AJ, Judd M, Fouracre R, Stewart B, Hepburn D (2011) Simultaneous measurement of partial discharges using IEC60270 and radio-frequency techniques. IEEE Trans Dielectr Electr Insul 18:444–455CrossRefGoogle Scholar
  15. 15.
    Evagorou D, Kyprianou A, Lewin P, Stavrou A, Efthymiou V, Metaxas A et al (2010) Feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network. IET Sci Meas Technol 4:177–192CrossRefGoogle Scholar
  16. 16.
    IEEE Standard C57.124-1991 (1992) IEEE recommended practice for the detection of partial discharge and the measurement of apparent charge in dry-type transformers, p 0_1Google Scholar
  17. 17.
    IEEE Standard C57.127-2000 (2000) Guide for the detection of acoustic emissions from partial discharges in oil-immersed power transformers, pp 1–24Google Scholar
  18. 18.
    IEEE Standard C37.301-2009 (2009) IEEE standard for high-voltage switchgear (above 1000 V) test techniques—partial discharge measurements, pp c1–63Google Scholar
  19. 19.
    IEEE Standard 519-1992 (1993) IEEE recommended practices and requirements for harmonic control in electrical power systems, pp 1–112Google Scholar
  20. 20.
    Nattrass DA (1988) Partial discharge measurement and interpretation. IEEE Electr Insul Mag 4:10–23CrossRefGoogle Scholar
  21. 21.
    Chen L-J, Tsao T-P, Lin Y-H (2005) New diagnosis approach to epoxy resin transformer partial discharge using acoustic technology. IEEE Trans Power Deliv 20:2501–2508CrossRefGoogle Scholar
  22. 22.
    Zhou X, Zhou C, Kemp I (2005) An improved methodology for application of wavelet transform to partial discharge measurement denoising. IEEE Trans Dielectr Electr Insul 12:586–594CrossRefGoogle Scholar
  23. 23.
    Hapeez MS et al (2013) An algorithm for identification of different types of partial discharge using harmonic orders. Przegląd Elektrotechniczny 89(5):36–42Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Nasir A. Al-geelani
    • 1
  • M. Afendi M. Piah
    • 2
  • Zulkurnain Abdul-Malek
    • 2
  1. 1.Department of Electrical and Electronics Engineering, Faculty of EngineeringAl-Madinah International UniversityShah AlamMalaysia
  2. 2.Institute of High Voltage and High Current, Faculty of Electrical EngineeringUniversiti Teknologi MalaysiaJohorMalaysia

Personalised recommendations