Review of recent research towards power cable life cycle management

Power cables are integral to modern urban power transmission and distribution systems. For power cable asset managers worldwide, a major challenge is how to manage effectively the expensive and vast network of cables, many of which are approaching, or have past, their design life. This study provides an in-depth review of recent research and development in cable failure analysis, condition monitoring and diagnosis, life assessment methods, fault location, and optimisation of maintenance and replacement strategies. These topics are essential to cable life cycle management (LCM), which aims to maximise the operational value of cable assets and is now being implemented in many power utility companies. The review expands on material presented at the 2015 JiCable conference and incorporates other recent publications. The review concludes that the full potential of cable condition monitoring, condition and life assessment has not fully realised. It is proposed that a combination of physics-based life modelling and statistical approaches, giving consideration to practical condition monitoring results and insulation response to in-service stress factors and short term stresses, such as water ingress, mechanical damage and imperfections left from manufacturing and installation processes, will be key to success in improved LCM of the vast amount of cable assets around the world.

[1]  S. Yasufuku.  Electroconductive polymers and their applications in Japan , 2001 .

[2]  Donald M. Hepburn,et al.  Online Monitoring and Diagnosis of HV Cable Faults by Sheath System Currents , 2017, IEEE Transactions on Power Delivery.

[3]  J. Densley,et al.  Ageing mechanisms and diagnostics for power cables - an overview , 2001 .

[4]  Ye Hui-sheng.  Analysis on Breakdown of Main Insulation on Middle Joint of 220 kV Power Cable , 2009 .

[5]  George Chen,et al.  Space charge and AC electric breakdown strength in polyethylene , 2015, 2015 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP).

[6]  Martin D. Judd,et al.  SDMF based interference rejection and PD interpretation for simulated defects in HV cable diagnostics , 2017, IEEE Transactions on Dielectrics and Electrical Insulation.

[7]  E. Gulski,et al.  Condition assessment of high voltage power cables , 2004, 2004 International Conference on Power System Technology, 2004. PowerCon 2004..

[8]  Paul Barringer.  Predict Failures: Crow-AMSAA 101 and Weibull 101 , 2004 .

[9]  Chris.  An introduction to fibre optic Intelligent Distributed Acoustic Sensing (iDAS) technology for power industry applications , 2015 .

[10]  Peter C. Kiessler,et al.  A critical look at the bathtub curve , 2003, IEEE Trans. Reliab..

[12]  Chengke Zhou,et al.  STOCHASTIC DYNAMIC PROGRAMMING APPROACH FOR PROACTIVE REPLACEMENT OF POWER CABLES , 2016 .

[13]  Yang Xu,et al.  Condition evaluation for 10 kV cables of Shenzhen Bureau based on CIGRE TB 358 , 2016, 2016 International Conference on Condition Monitoring and Diagnosis (CMD).

[14]  Matthieu Michel,et al.  On-line partial discharge monitoring in medium voltage underground cables , 2009 .

[15]  I. Peretz,et al.  Individual Differences in Rhythmic Cortical Entrainment Correlate with Predictive Behavior in Sensorimotor Synchronization , 2016, Scientific Reports.

[16]  E. Gulski,et al.  Use of dissipation factor for life consumption assessment and future life modeling of oil-filled high-voltage power cables , 2012, IEEE Electrical Insulation Magazine.

[17]  Daniel Gebhardt,et al.  PD testing and monitoring of HV cable systems , 2015 .

[18]  JJ Walker,et al.  Electrical stress distribution in cable accessories when testing at different frequencies , 2010, 2010 International Conference on High Voltage Engineering and Application.

[19]  Chengke Zhou,et al.  Multiple Correspondence Analysis to Study Failures in a Diverse Population of a Cable , 2017, IEEE Transactions on Power Delivery.

[20]  Y Gill,et al.  Development of an electrical cable replacement simulation model to aid with the management of aging underground electric cables , 2011, IEEE Electrical Insulation Magazine.

[21]  Shengtao Li,et al.  Space Charge Modulated Electrical Breakdown , 2016, Scientific Reports.

[22]  Chengke Zhou,et al.  Partial discharge pulse propagation in power cable and partial discharge monitoring system , 2014, IEEE Transactions on Dielectrics and Electrical Insulation.

[23]  M. D. Judd,et al.  Fault location and diagnosis in a medium voltage EPR power cable , 2013, IEEE Transactions on Dielectrics and Electrical Insulation.

[24]  Ross MacKinlay.  Managing an aging underground high-voltage cable network , 1990 .

[25]  Chengke Zhou,et al.  Analysis of Significant Factors on Cable Failure Using the Cox Proportional Hazard Model , 2014, IEEE Transactions on Power Delivery.

[26]  R. Brook,et al.  Cumulative Damage in Fatigue: A Step towards Its Understanding , 1969 .

[27]  Luuk C. Rietveld,et al.  Reducing customer minutes lost by anomaly detection , 2012 .

[28]  G. Mazzanti.  Effects of the combination of electro-thermal stress, load cycling and thermal transients on polymer-insulated high voltage ac cable life , 2005, IEEE Power Engineering Society General Meeting, 2005.

[29]  Wei Shen,et al.  Investigation of Economic Feasibility of Different Rules of Insulation Diagnosis , 2009, IEEE Transactions on Dielectrics and Electrical Insulation.

[30]  Giovanni Mazzanti,et al.  Challenge of fault location on long submarine power cables , 2016 .

[31]  Yang Yang,et al.  Comparison of the Weibull and the Crow-AMSAA Model in Prediction of Early Cable Joint Failures , 2015, IEEE Transactions on Power Delivery.

[33]  Martin D. Judd,et al.  Rough set theory applied to pattern recognition of Partial Discharge in noise affected cable data , 2017, IEEE Transactions on Dielectrics and Electrical Insulation.

[34]  Elisabeth Ilskog,et al.  The Zanzibar blackout : A case study on consequences from an electricity power crisis , 2011 .

[35]  Yong Sun,et al.  A review on degradation models in reliability analysis , 2010, WCE 2010.

[36]  G. Mazzanti,et al.  The combination of electro-thermal stress, load cycling and thermal transients and its effects on the life of high voltage ac cables , 2009, IEEE Transactions on Dielectrics and Electrical Insulation.

[37]  B. Lanz,et al.  Application of medium voltage cable predictive diagnostics , 2008, Conference Record of 2008 54th Annual Pulp and Paper Industry Technical Conference.

[38]  Jun Hu,et al.  Evaluation of polypropylene/polyolefin elastomer blends for potential recyclable HVDC cable insulation applications , 2015, IEEE Transactions on Dielectrics and Electrical Insulation.

[39]  Donald M. Hepburn,et al.  On-line PD detection and localization in cross-bonded HV cable systems , 2014, IEEE Transactions on Dielectrics and Electrical Insulation.

[40]  Yasuo Suzuoki,et al.  Robustness Assessment of Life-Cycle-Management of CV Cables based on Degradation Diagnosis , 2007 .

[41]  JYancy Gill.  Forecasting Underground Electric Cable Faults Using the Crow-AMSAA Model , 2011 .

[42]  Chengke,et al.  On-line Monitoring and Trending Analysis of Dielectric Losses in Cross- bonded High Voltage Cable Systems , 2015 .

[43]  Gian Carlo Montanari,et al.  Aging phenomenology and modeling , 1993 .

[44]  C. Minto,et al.  OptaSense® Distributed Acoustic Sensing (DAS) System for the Power Network - Integrated SMART-Sensing REAL TIME MONITORING , 2015 .