Improving maintenance scheduling with condition monitoring on the electric distribution grid : An economic analysis comparing corrective and predictive maintenance

Abstract: A growing use of sensors on the electric grid has opened the door to new methods of asset management: Distribution System Operators are now looking into conditionbased maintenance, as opposed to the traditional corrective or time-based methods. As an emerging field, the methodology must be constructed from the ground, with the little data available. The focus is put on cross-linked polyethylene medium voltage overhead lines (XLPE MVOHL)asthe asset to manage, and the aim of this work is to study under which conditions the use of sensors to improve maintenance scheduling on those lines is economically profitable. Solving this problem starts with a necessary review of key ageing mechanisms of XLPE MV overhead lines, followed by the identification of sensors which can monitor the quantities behind these mechanisms. Statistical models for the lifetime of electrical assets and economic models for the analysis of investments are also described. From this preliminary study, a condition-based maintenance methodology was devised using the concept of Health Index to gather data from multiple types of sensors into one unique indicator. Using existing literature, this health index is used to dynamically estimate the failure rate of the line. This failure rate is the key to condition-based maintenance scheduling: maintenance operations are triggered when the failure rate reaches a threshold. Selecting one ageing mechanism- electrical stress-, and one type of sensorpartial discharge inductive sensors-, a Python simulation was built (and is shared at the end of this thesis) allowing to compare the cost of predictive maintenance to the cost of corrective maintenance over several decades, with the key parameters clearly identified and analysed. Beyond the methodology in itself, the main result of the work is that the use of sensors is economically profitable in most of the studied conditions. This project also reveals the strong influence of some parameters on this profitability: condition monitoring is particularly justified for short-lived assets, with a narrow distribution of failures. The failure rate threshold must be set carefully as it has a major impact on the analysis: setting it too high leads to an unprofitable scenario.