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Selecting a condition monitoring system for enhancing effectiveness of power transformer maintenance

Research output: Contribution to journalArticle

Purpose: The purpose of this paper is to propose a model for assisting in the decision-making process for acquiring a condition monitoring (CM) system for an oil-immersed power transformer in order to improve its maintainability.

Design/methodology/approach: The proposed model is based on the analytic hierarchy process. The assessment was performed by pairwise comparisons, and a sensitivity analysis (what-if analysis) was used to identify the implications of changing the criteria weights. In order to select the criteria and alternatives, a search was conducted for the power transformer failure modes, monitored parameters and CM technologies.

Findings: The proposed model provides a structured solution for a complex problem: deciding the best combination of technologies for CM of power transformers.

Research limitations/implications: Because the pairwise comparisons were done only by the author, the results may need to be improved with the assessment of more experts. Also, it was done for a specific type of transformer; it might be necessary to customise the alternatives for other cases. Finally, as a future consideration, more levels can be added to the hierarchy to improve the accuracy of the model.

Practical implications: The power transformer is an asset where the most appropriate maintenance strategy for it is condition-based maintenance. In order to improve its maintainability, it is recommendable to improve its testability and diagnosability. For achieving this goal, the maintenance personnel have to decide the best combination of technologies for CM. The methodology developed can assist the decision makers to select the most appropriate cost-benefit strategy.

Originality/value: The paper presents a structured and generic method of selecting the most appropriate CM system for power transformers.
Original languageEnglish
Pages (from-to)400-414
Number of pages15
JournalJournal of Quality in Maintenance Engineering
Volume23
Issue number4
Early online date16 Oct 2017
DOIs
Publication statusEarly online - 16 Oct 2017

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