Partial discharge and noise separation by means of spectral-power clustering techniques

Partial Discharges (PDs) are one of the most important classes of ageing processes that occur within electrical insulation. The measurement of PDs is useful in the diagnosis of electrical equipment because PDs activity is related to different ageing mechanisms. Classical Phase-Resolved Partial Discharge (PRPD) patterns are able to identify PD sources when they are related to a clear degradation process and when the noise level is low compared to the amplitudes of the PDs. However, real insulation systems usually exhibit several PD sources and the noise level is high, especially if measurements are performed on-line. High-frequency (HF) sensors and advanced signal processing techniques have been successfully applied to identify these phenomena in real insulation systems. In this paper, spectral power analyses of PD pulses and the spectral power ratios at different frequencies were calculated to classify PD sources and noise by means of a graphical representation in a plane. This technique is a flexible tool for noise identification and will be useful for pulse characterization.

[1]  P.H.F. Morshuis,et al.  Degradation of solid dielectrics due to internal partial discharge: some thoughts on progress made and where to go now , 2005, IEEE Transactions on Dielectrics and Electrical Insulation.

[2]  L. Niemeyer.  A generalized approach to partial discharge modeling , 1995 .

[3]  A Cavallini,et al.  Diagnostic of HVDC systems using partial discharges , 2011, IEEE Transactions on Dielectrics and Electrical Insulation.

[4]  H Lee Willis,et al.  Aging Power Delivery Infrastructures , 2000 .

[5]  P.H.F. Morshuis,et al.  Partial discharge mechanisms: Mechanisms leading to breakdown, analyzed by fast electrical and optical measurements , 1993 .

[6]  G. Robles,et al.  A Partial Discharges acquisition and statistical analysis software , 2012, 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[7]  J. A. Hunter,et al.  Discrimination of multiple PD sources using wavelet decomposition and principal component analysis , 2011, IEEE Transactions on Dielectrics and Electrical Insulation.

[8]  Guillermo Robles,et al.  Partial discharge pulse shape recognition using an inductive loop sensor , 2010 .

[9]  Gian Carlo Montanari,et al.  A new approach to the diagnosis of solid insulation systems based on PD signal inference , 2003 .

[10]  H. Okubo,et al.  A novel technique for partial discharge and breakdown investigation based on current pulse waveform analysis , 2005, IEEE Transactions on Dielectrics and Electrical Insulation.

[11]  G. Montanari,et al.  Digital detection and fuzzy classification of partial discharge signals , 2002 .

[12]  Thierry Lebey,et al.  Development of a new off-line test procedure for low voltage rotating machines fed by adjustable speed drives (ASD) , 2003 .

[13]  G.C. Stone,et al.  Electrical insulation for rotating machines-design, evaluation, aging, testing, and repair - Book Review , 2004, IEEE Electrical Insulation Magazine.

[14]  F. H. Kreuger,et al.  Partial Discharge Detection in High Voltage Equipment , 1990 .