An investigative study into the sensitivity of different partial discharge φ-q-n pattern resolution sizes on statistical neural network pattern classification

This paper investigates the sensitivity of statistical fingerprints to different phase resolution (PR) and amplitude bins (AB) sizes of partial discharge (PD) φ-q-n (phase-amplitude-number) patterns. In particular, this paper compares the capability of the ensemble neural network (ENN) and the single neural network (SNN) in recognizing and distinguishing different resolution sizes of φ-q-n discharge patterns. The training fingerprints for both the SNN and ENN comprise statistical fingerprints from different φ-q-n measurements. The result shows that there exists statistical distinction for different PR and AB sizes on some of the statistical fingerprints. Additionally, the ENN and SNN outputs change depending on training and testing with different PR and AB sizes. Furthermore, the ENN appears to be more sensitive in recognizing and discriminating the resolution changes when compared with the SNN. Finally, the results are assessed for practical implementation in the power industry and benefits to practitioners in the field are highlighted.

[1]  Shi Zhi-xia.  FEATURE OF NEW STANDARD IEC 60270 FOR PARTIAL DISCHARGE MEASUREMENT AND ITS INFLUENCE ON PD MEASURING ACTIVITY , 2001 .

[2]  Mohammad Oskuoee,et al.  Partial discharge pattern recognition via sparse representation and ANN , 2015, IEEE Transactions on Dielectrics and Electrical Insulation.

[3]  Hazlee Azil Illias,et al.  Partial discharge classifications: Review of recent progress , 2015 .

[4]  Pádraig Cunningham,et al.  Stability problems with artificial neural networks and the ensemble solution , 2000, Artif. Intell. Medicine.

[5]  Antonio Torralba,et al.  Time domain analysis of partial discharges envelope in medium voltage XLPE cables , 2015 .

[6]  Mohammad Oskuoee,et al.  Improving pattern recognition accuracy of partial discharges by new data preprocessing methods , 2015 .

[7]  E. Gulski,et al.  Neural networks as a tool for recognition of partial discharges , 1993 .

[8]  Magdy M. A. Salama,et al.  Partial discharge pattern classification using the fuzzy decision tree approach , 2005, IEEE Transactions on Instrumentation and Measurement.

[9]  E Gulski,et al.  Computer-aided analysis of discharge patterns , 1990 .

[10]  Scott G. McMeekin,et al.  Application of an ensemble neural network for classifying partial discharge patterns , 2014 .

[11]  N.G. Trinh,et al.  Partial discharge XIX: discharge in air part I: physical mechanisms , 1995, IEEE Electrical Insulation Magazine.

[12]  A. A. Al-Arainy,et al.  Partial discharge pattern analysis using support vector machine to estimate size and position of metallic particle adhering to spacer in GIS , 2014 .

[13]  B. Karthikeyan,et al.  ART 2 - an unsupervised neural network for PD pattern recognition and classification , 2006, Expert Syst. Appl..

[14]  A. Krivda,et al.  Automated recognition of partial discharges , 1995 .

[15]  R. Bartnikas,et al.  Partial discharge pulse pattern recognition using Hidden Markov Models , 2004, IEEE Transactions on Dielectrics and Electrical Insulation.

[16]  Cheng-Chien Kuo.  Artificial identification system for transformer insulation aging , 2010, Expert Syst. Appl..

[17]  Ricardo Albarracín Sánchez,et al.  On the Use of Monopole Antennas for Determining the Effect of the Enclosure of a Power Transformer Tank in Partial Discharges Electromagnetic Propagation , 2016, Sensors.

[18]  T. Babnik,et al.  Data mining on a transformer partial discharge data using the self-organizing map , 2007, IEEE Transactions on Dielectrics and Electrical Insulation.

[19]  P.A. Crossley,et al.  Partial discharge pattern recognition based on 2-D wavelet transform and neural network techniques , 2002, IEEE Power Engineering Society Summer Meeting,.