Open AccessLifetime estimation and diagnosis of XLPE used in HV insulation cables under thermal ageing: arithmetic sequences optimised by genetic algorithms approach
Author(s) -
Bessissa Lakhdar,
Boukezzi Larbi,
Mahi Djillali,
Boubakeur Ahmed
Publication year - 2017
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2016.0491
Subject(s) - genetic algorithm , schedule , reliability engineering , margin (machine learning) , preventive maintenance , algorithm , voltage , work (physics) , estimation , computer science , engineering , mechanical engineering , electrical engineering , machine learning , systems engineering , operating system
The arithmetic sequences optimised by genetic algorithms have been applied in the lifetime estimation and diagnosis of cross‐linked polyethylene (XLPE) high‐voltage insulation under thermal ageing. To know an idea of the cable capacity to work without failure, it is necessary to predict the future state of the electrical insulation. If the estimation is very good, the authors can start to schedule maintenance tasks of electrical system and find preventive solutions in early time. A large amount of money can be saved if they take appropriate actions. The developed model gives results in good agreement with the experimental results, with an acceptable error margin. The authors also applied the same models in the diagnosis of the high‐voltage insulation to plan the preventive maintenance actions. The decision is taken from the carried out previous experimental measurements of XLPE properties. The developed approaches are able to make the diagnosis and the classification of the insulation state. The results of modelling, prediction and diagnosis presented in this work demonstrate the effectiveness of the used method.