Reliability modelling for electricity transmission networks using maintenance records

Li, Fengfeng, Cholette, Michael E., & Ma, Lin (2016) Reliability modelling for electricity transmission networks using maintenance records. In Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015), Springer International Publishing, Tampere, Finland, pp. 397-406.

[img] PDF (311kB)
Administrators only until 26 March 2017 | Request a copy from author

View at publisher


Maintenance decisions for transmission network assets (TNAs) require accurate reliability prediction of complex repairable systems. There are a number of factors influencing the reliability prediction for TNAs, which includes structure characteristics (e.g. conductor type), voltage, load, and the operating environment (mechanical loading, wind, temperature, pollutants and humidity). The reliability analysis and prediction is complicated by the fact that TNAs are linear assets (as opposed to discrete assets) which require specific modelling approaches for reliability prediction. This paper details a new reliability prediction model for TNAs. Another challenge is that transmission network is hardly fail. Instead of using outage data, where most reliability model used, failure times were identified through extracting significant unplanned maintenance events for critical failure modes. A regression tree based grouping analysis was utilized to analyse the influences by variety of factors on future unplanned maintenance. These results were then used to build the reliability prediction model allowing a decision maker to have an estimate of future unplanned maintenance requirements. A case study using real industry data was conducted to test the proposed reliability prediction model. The results demonstrate the feasibility of using this approach for TNA maintenance decision support.

Impact and interest:

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 99980
Item Type: Conference Paper
Refereed: No
Keywords: maintenance decision support, regression tree, reliability
DOI: 10.1007/978-3-319-27064-7_38
ISBN: 9783319270623
ISSN: 2195-4356
Divisions: Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2016 Springer International Publishing Switzerland
Copyright Statement: The final publication is available at Springer via
Deposited On: 13 Oct 2016 22:40
Last Modified: 27 Jan 2017 03:07

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page