Decision tree rules for insulation condition assessment of pre-molded power cable joints with artificial defects

A decision tree method is presented that can judge the initial and final stages of insulation degradation for pre-molded power cable joints with air gap and void defects. According to the partial discharge (PD) of eight experimental subjects, it is validated that the discharge phase changes toward the voltage zero-crossing area and becomes wider. This well-known phenomenon can be applied to assess the transition of insulation status by the cluster theory. Subsequently, the Gini index coefficient and information gain of the decision tree algorithm yield the insulation status diagnostic rules. The result indicates an overall recognition rate of 85% in training data. Furthermore, the proposed diagnostic method has successfully detected the alert of the final stage prior to insulation breakdown for two additional experimental subjects. These decision tree rules are feasible and economical for an online PD monitoring device to achieve condition-based maintenance in distribution power cables.

[1]  S. Mohammad Shahrtash,et al.  On-line decision tree-based insulation assessment employing mathematical morphology filters for HV cables , 2013, IEEE Transactions on Dielectrics and Electrical Insulation.

[2]  I-Hua Chung,et al.  Classification of partial discharge patterns in GIS using adaptive neuro-fuzzy inference system , 2014 .

[3]  Simon M. Rowland,et al.  Electrical treeing and reverse tree growth in an epoxy resin , 2017, IEEE Transactions on Dielectrics and Electrical Insulation.

[4]  Min Wu,et al.  An overview of state-of-the-art partial discharge analysis techniques for condition monitoring , 2015, IEEE Electrical Insulation Magazine.

[5]  J. Densley,et al.  Characteristics of PD pulses in electrical trees and interfaces in extruded cables , 2001 .

[6]  S. Rowland,et al.  The dynamic character of partial discharge in epoxy resin at different stages of treeing , 2016, 2016 IEEE International Conference on Dielectrics (ICD).

[7]  Yu-Hsun Lin,et al.  Novel trend of "l" shape in PD pattern to judge the appropriate crucial moment of replacing cast-resin current transformer , 2008, IEEE Transactions on Dielectrics and Electrical Insulation.

[8]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[9]  C. Katz,et al.  Comparative Laboratory Evaluation of Premolded Joints for Medium Voltage Cables , 2008, IEEE Transactions on Power Delivery.

[10]  Wojciech Koltunowicz,et al.  Increased operation reliability of HV apparatus through PD monitoring , 2016, IEEE Transactions on Dielectrics and Electrical Insulation.

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

[12]  E. Gulski,et al.  PD knowledge rules for insulation condition assessment of distribution power cables , 2005, IEEE Transactions on Dielectrics and Electrical Insulation.

[13]  Hazlee Azil Illias,et al.  High noise tolerance feature extraction for partial discharge classification in XLPE cable joints , 2017, IEEE Transactions on Dielectrics and Electrical Insulation.

[14]  Chien-Kuo Chang,et al.  The Use of Partial Discharges as an Online Monitoring System for Underground Cable Joints , 2011, IEEE Transactions on Power Delivery.

[15]  Yasuo Suzuoki,et al.  Model for partial discharges associated with treeing breakdown: II. tree growth affected by PDs , 2000 .

[16]  K X Lai,et al.  Application of data mining on partial discharge part I: predictive modelling classification , 2010, IEEE Transactions on Dielectrics and Electrical Insulation.

[17]  H. Hirose,et al.  Diagnosis of electric power apparatus using the decision tree method , 2008, IEEE Transactions on Dielectrics and Electrical Insulation.

[18]  Chengke Zhou,et al.  Application of K-Means method to pattern recognition in on-line cable partial discharge monitoring , 2013, IEEE Transactions on Dielectrics and Electrical Insulation.