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Predictive Maintenance and Data Analysis in Ellevio's Distribution Grid
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
2019 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

As part of the international EU project Integrid, Ellevio has started a pilot project where measurement equipment has been installed in secondary substations in Stockholm Royal Seaport. The purpose of the pilot project is to improve operation, maintenance and future planning of the grid. This thesis focuses on predictive maintenance and data analysis of power quality parameters and aims to cover several areas within these two categories to answer the following research questions:

-In what ways can the life-cycle and faults of components be used as a basis for predictive maintenance?

-Are there any measurable parameters - currently being monitored or not - that can be useful for Ellevio in terms of maintenance and grid development?

-Is the installation of more monitoring systems worthwhile, and if so, what should be monitored?

-Is it possible to find indicators that allow for predictive maintenance to reduce the interruption time and maintenance cost?

The main results are that the focus should lie on middle voltage (MV) cables due to its significant contribution to SAIDI and SAIFI. Monitoring partial discharges in MV cables could potentially lower the frequency of faults. A study needs to be conducted to establish the extent of partial discharges as a source of faults. Furthermore harmonic content was found to have a large impact on transformer losses and this parameter should be considered when dimensioning new secondary substations. Another parameter that had interesting results is the current unbalance which led to high neutral currents and caused high losses.

Place, publisher, year, edition, pages
2019.
Series
UPTEC E, ISSN 1654-7616 ; 19 026
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:uu:diva-398597OAI: oai:DiVA.org:uu-398597DiVA, id: diva2:1376087
External cooperation
Ellevio AB
Supervisors
Examiners
Available from: 2019-12-12 Created: 2019-12-07 Last updated: 2019-12-12Bibliographically approved

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