A New Partial Discharge Signal Denoising Algorithm Based on Adaptive Dual-Tree Complex Wavelet Transform

Denoising is a key step in diagnosis and evaluation of partial discharge (PD) signals in power transformers. In this paper, a new PD signal denoising algorithm is presented, which is based on the combination of dual-tree complex wavelet transform (DTCWT) and adaptive singular value decomposition (ASVD). This new algorithm, which is introduced as adaptive DTCWT (ADTCWT), was evaluated through simulations and experimental tests. ADTCWT was employed in denoising from PD signals based on the selection of best singular values in each DTCWT level decomposition, corresponding to PD signal and noise. The superior performance of the ADTCWT algorithm was demonstrated using various indices in comparison with those of DTCWT and ASVD methods in noise reduction, besides preserving time of arrival of PD signals and PD localization.

[1]  Julio E. Posada-Roman,et al.  Instrumentation System for Location of Partial Discharges Using Acoustic Detection With Piezoelectric Transducers and Optical Fiber Sensors , 2014, IEEE Transactions on Instrumentation and Measurement.

[2]  A. K. Lazarevich,et al.  Partial Discharge Detection and Localization in High Voltage Transformers Using an Optical Acoustic Sensor , 2003 .

[3]  Khaled Bashir Shaban,et al.  Wavelet Transform With Histogram-Based Threshold Estimation for Online Partial Discharge Signal Denoising , 2015, IEEE Transactions on Instrumentation and Measurement.

[4]  S. Shahrtash,et al.  Partial discharge de-noising employing adaptive singular value decomposition , 2014, IEEE Transactions on Dielectrics and Electrical Insulation.

[5]  E. Gockenbach,et al.  A novel method for ultra-high-frequency partial discharge localization in power transformers using the particle swarm optimization algorithm , 2013, IEEE Electrical Insulation Magazine.

[6]  Jieming Zhang,et al.  Feature extraction of GIS partial discharge signal based on S-transform and singular value decomposition , 2017 .

[7]  Xiandong Ma,et al.  Interpretation of wavelet analysis and its application in partial discharge detection , 2002 .

[8]  Pengfei Li,et al.  An Ultrahigh Frequency Partial Discharge Signal De-Noising Method Based on a Generalized S-Transform and Module Time-Frequency Matrix , 2016, Sensors.

[9]  S.M. Shahrtash,et al.  Comparing denoising performance of DWT,WPT, SWT and DT-CWT for Partial Discharge signals , 2008, 2008 43rd International Universities Power Engineering Conference.

[10]  Jeongtae Kim,et al.  Partial discharge location using 2 sensors and optical fibers , 2012, 2012 IEEE International Conference on Condition Monitoring and Diagnosis.

[11]  J. Ramirez-Niño,et al.  Analysis of partial electrical discharges in insulating materials through the wavelet transform , 1998 .

[12]  Stanislav Misak,et al.  A novel approach of partial discharges detection in a real environment , 2016, 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC).

[13]  Y. Yang,et al.  Random interpolation average for signal denoising , 2010 .

[14]  Massimo Lazzaroni,et al.  Analysis of PD signal by wavelet transform , 2000, Proceedings of the 17th IEEE Instrumentation and Measurement Technology Conference [Cat. No. 00CH37066].

[15]  R. Bartnikas,et al.  On-line detection and measurement of partial discharge signals in a noisy environment , 2008, IEEE Transactions on Dielectrics and Electrical Insulation.

[16]  L. Hao,et al.  Improving detection sensitivity for partial discharge monitoring of high voltage equipment , 2008 .

[17]  Xiandong Ma,et al.  Automated wavelet selection and thresholding for PD detection , 2002 .

[18]  Jian Li,et al.  Scale dependent wavelet selection for de-noising of partial discharge detection , 2010, IEEE Transactions on Dielectrics and Electrical Insulation.

[19]  Yu-Hsun Lin.  Using K-Means Clustering and Parameter Weighting for Partial-Discharge Noise Suppression , 2011, IEEE Transactions on Power Delivery.

[20]  S. M. Shahrtash,et al.  Feature-oriented de-noising of partial discharge signals employing mathematical morphology filters , 2012, IEEE Transactions on Dielectrics and Electrical Insulation.

[21]  Hui Ma,et al.  Self-adaptive partial discharge signal de-noising based on ensemble empirical mode decomposition and automatic morphological thresholding , 2014, IEEE Transactions on Dielectrics and Electrical Insulation.

[22]  Yanyang Zi,et al.  Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform , 2010 .

[23]  Whei-Min Lin,et al.  Study of Partial Discharge Measurement in Power Equipment Using Acoustic Technique and Wavelet Transform , 2007, IEEE Transactions on Power Delivery.

[24]  Ju Tang,et al.  Suppressing white‐noise in partial discharge measurements—part 1: construction of complex Daubechies wavelet and complex threshold , 2010 .

[25]  M. R. Petraglia,et al.  A new wavelet selection method for partial discharge denoising , 2015 .

[26]  Paul Lewin,et al.  Wavelet packet denoising for online partial discharge detection in cables and its application to experimental field results , 2006 .

[27]  Chengke Zhou,et al.  Second generation wavelet transform for data denoising in PD measurement , 2007, IEEE Transactions on Dielectrics and Electrical Insulation.

[28]  Dong Wang,et al.  Entropy-Based Method of Choosing the Decomposition Level in Wavelet Threshold De-noising , 2010, Entropy.

[29]  L. Satish,et al.  Wavelet-based denoising of partial discharge signals buried in excessive noise and interference , 2003 .

[30]  Julio E. Posada-Roman,et al.  Two-stage processing system for the detection and on-site localization of acoustic emissions , 2015, 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings.

[31]  R. Bartnikas,et al.  De-noising of partial discharge signal using eigen-decomposition technique , 2008, IEEE Transactions on Dielectrics and Electrical Insulation.

[32]  Richard Baraniuk,et al.  The Dual-tree Complex Wavelet Transform , 2007 .

[33]  Jian Li,et al.  Wavelet de-noising of partial discharge signals based on genetic adaptive threshold estimation , 2012, IEEE Transactions on Dielectrics and Electrical Insulation.

[34]  S. Shahrtash,et al.  Partial discharge de-noising employing improved singular value decomposition , 2012, 2012 Annual Report Conference on Electrical Insulation and Dielectric Phenomena.

[35]  B. Chatterjee,et al.  Cross-wavelet transform as a new paradigm for feature extraction from noisy partial discharge pulses , 2010, IEEE Transactions on Dielectrics and Electrical Insulation.

[36]  Ju Tang,et al.  Suppressing white-noise in partial discharge measurements part 2: the optimal de-noising scheme , 2010 .

[37]  Ramy Hussein,et al.  Denoising of acoustic partial discharge signals corrupted with random noise , 2016, IEEE Transactions on Dielectrics and Electrical Insulation.

[38]  Chengrong Li,et al.  Location of PDs inside transformer windings using UHF methods , 2014, IEEE Transactions on Dielectrics and Electrical Insulation.

[39]  M. Willjuice Iruthayarajan,et al.  Employing the mathematical morphology filters for de-noising Partial Discharge signals , 2014, 2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014].

[40]  John J. Soraghan,et al.  Detection of PD utilizing digital signal processing methods. Part 3: Open-loop noise reduction , 2001 .

[41]  Jian Li,et al.  Wavelet De-Noising for PD UHF Signals Based on Adaptive Thresholding by Genetic Algorithm , 2008, 2008 Annual Report Conference on Electrical Insulation and Dielectric Phenomena.

[42]  S. Tenbohlen,et al.  Detection and location of partial discharges in power transformers using acoustic and electromagnetic signals , 2008, IEEE Transactions on Dielectrics and Electrical Insulation.

[43]  S. Sumathi,et al.  A novel algorithm for denoising of simulated partial discharge signals using Adaptive wavelet thresholding methods , 2015, 2015 2nd International Conference on Electronics and Communication Systems (ICECS).

[44]  M. Vitelli,et al.  Analysis of ultrawide-band detected partial discharges by means of a multiresolution digital signal-processing method , 2000 .