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Comparison of Wavelet, Stationary Wavelet and Wavelet Packet Methods for De-noising of Partial Discharge on Power Cable System

  • Arunjothi RajendranEmail author
  • Thirumurthy
  • K. P. Meena
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 598)

Abstract

The failure of Power Cable systems is mainly attributed to Partial Discharge activity in power Cable insulation and accessories such as terminations and joints. Different types of sensors are available for online and offline monitoring of Partial discharge activity. However the main difficulty involved in the Partial discharge measurement of Power Cable system at site is to eliminate the interference from other external and internal high frequency noisy signals. In this paper, de noising the PD signals by the wavelet transforms, stationary wavelet and wavelet packets techniques are compared for its merits and demerits. The method used for selection of mother wavelet is based on minimum entropy criteria. The two types of mother wavelet debauchees and symlet are used. The effectiveness of level dependent hard thresholding method in retrieving the original partial discharge signals over the other conventional thresholding methods is also analyzed. The laboratory measured Partial discharge signals are mixed with white Gaussian noise and discrete spectral interference (DSI) signals in such a way that the partial discharge signals are completely immersed in the simulated noises. The partial discharge signals are measured for power cables system with Improper Stress control tubing termination.

Keywords

Partial Discharge Power Cable Wavelet Noise elimination 

References

  1. 1.
    Mortazavi, S.H., Shahrtash, S.M.: Comparing denoising performance of DWT, WPT, SWT and DT-CWT for partial discharge signals. In: 43rd International Universities Power Engineering Conference, UPEC 2008 (2008)Google Scholar
  2. 2.
    Madhu, S., Amit, S., Shivam, G., Sumathi, S., Vidya, H.A.: Level dependent wavelet selection for denoising of partial discharge signals simulated by DEP and DOP Models. In: International Conference on Computer Science and Mechanical Engineering, 10th August 2014, Jaipur, India (2014)Google Scholar
  3. 3.
    Kyprianou, A., Lewin, P.L., Efthimiou, V., Stavrou, A., Georghiou, G.E.: Wavelet packet denoising for online partial discharge detection in cables and its application to experimental field results. Meas. Sci. Technol. 17, 2367–2379 (2006)CrossRefGoogle Scholar
  4. 4.
    Hussein, R., Shaban, K.B., El-Hag, A.H.: Wavelet transform with histogram based threshold estimation for online partial discharge signal denoising. IEEE Trans. Instrum. Meas. 64(12), 3601–3614 (2015)CrossRefGoogle Scholar
  5. 5.
    Ray, P., Maitra, A.K., Basuray, A.: Entropy-based wavelet de-noising for partial discharge measurement application. In: 2016 IEEE First International Conference on Control, Measurement and Instrumentation (CMI), India (2016)Google Scholar
  6. 6.
    Jayakrishnan, M., Rao, B.N., Meena, K.P., Arunjothi, R., et al.: Optimum threshold estimator for de-noising partial discharge signal using wavelet transform technique. In: IEEE International Conference CATCON 2015 (2015)Google Scholar
  7. 7.
    Evagorouet, D., et al.: Evaluation of partial discharge denoising using the wavelet packets transform as a preprocessing step for classification. In: CEIDP 2008 (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Central Power Research InstituteBangaloreIndia

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