Paper Info
Title
Domain Adaptation for Ageing State Recognition of Cables used in Power Systems.
Abstract
As power cable systems play a critical role with respect to the reliability of power grids, it is necessary to adopt predictive maintenance techniques to prevent their damage. Especially for very expensive equipment used in power generation or smart grids, failure must be avoided. Partial Discharge (PD) phenomenon is considered as one of the main cause of deterioration of power cables insulation and reduce the cable lifetime. Thus, PD detection is an effective way to evaluate the degradation state of cable insulation. In this study, PD data are extracted from two different environments : a low voltage and high voltage cable using two different experimental devices. Original feature extraction, ranking and selection methods are implemented to design optimized Support Vector Machines classifiers (SVMs) that attribute an ageing state to the cable insulation for both cable types. Given the great interest of transferring a cable diagnosis model from one cable to any other cable type a domain adaptation technique is implemented. Recognition rate exceeding 99% are achieved when a classifier built in one environment is used to predict the ageing state of a new cable type based on PD data acquired in different conditions. The methodology presented in this work could be applied in realistic settings, for the monitoring of different cable types used in power grid system based on PD measurements.
Year
DOI
Venue
2019
10.1109/ICMLA.2019.00276
ICMLA
Field
DocType
Citations 
Partial discharge,High-voltage cable,Smart grid,Computer science,Power cable,Electric power system,Low voltage,Artificial intelligence,Predictive maintenance,Reliability engineering,Machine learning,Electricity generation
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Nathalie Morette100.34
Thierry Ditchi200.34
Y. Oussar329426.32