Lifetime estimation and diagnosis of XLPE used in HV insulation cables under thermal ageing: arithmetic sequences optimised by genetic algorithms approach. Issue 10 (5th July 2017)

• Record Type: Journal Article
• Authors: Bessissa, Lakhdar; Boukezzi, Larbi; Mahi, Djillali; Boubakeur, Ahmed
• Is Part Of: IET generation, transmission & distribution. Volume 11:Issue 10(2017)
• Page Start: 2429
• Page End: 2437
• Publication Date: 2017-07-05
• Subjects: XLPE insulation -- power cable insulation -- ageing -- genetic algorithms -- preventive maintenance; XLPE diagnosis -- XLPE lifetime estimation -- HV insulation cables -- thermal ageing -- arithmetic sequence -- genetic algorithms approach -- cross‐linked polyethylene high‐voltage insulation diagnosis -- cross‐linked polyethylene high‐voltage insulation lifetime estimation -- electrical insulation state prediction -- electrical system maintenance task -- preventive maintenance action -- insulation state diagnosis -- insulation state classification; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2016.0491
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : 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.