Ageing behaviour of medium-voltage substations

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Conference name
CIRED 2019
Conference date
3-6 June 2019
Conference location
Madrid, Spain
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Dalamaras, Petros, University of Wuppertal, Germany
Zdrallek, Markus , University of Wuppertal, Germany
Groß, Ulrich, Rheinische NETZGesellschaft mbH, Germany
Knapp, Martin, Rheinische NETZGesellschaft mbH, Germany
Schulze, Heike, Mitteldeutsche Netzgesellschaft Strom mbH, Germany
Klöckner, Patrick, MVV Netze GmbH, Germany
Straube, Axel, SWS Netze Solingen GmbH, Germany
Gawlitta, Ralf, SWS Netze Solingen GmbH, Germany
The ageing infrastructure, in conjunction with incentives to improve power quality and reliability of supply, pose considerable challenges for distribution system operators (DSOs). More and more components of electric power grids are on the verge of reaching their assumed maximum service life. Today's DSOs apply modern and optimised maintenance, as well as replacement strategies based on the knowledge of the actual condition of the operating electrical equipment. This enables an efficient and fair distribution of the limited budget over the individual grid areas. In addition to the condition of medium-voltage (MV) substations, knowledge of their ageing behaviour is essential for long-term, well-founded replacement strategies. The asset simulation tools used by DSOs are mostly based on the bathtub curve, which is intended to describe the hypothetical failure rate in the distribution grid versus time. This paper presents a new and innovative approach to derive condition-dependent ageing curves as a valid basis for decision making from the results of inspection data of MV substations. The more than 100.000 inspection results, available from eleven different DSOs, serve as a basis for this. In order to verify the ageing behaviour determined from the inspection data, extensive additional laboratory tests have been carried out on 84 MV switchgears. For the first time, significant findings regarding the actual ageing behaviour of MV substations can be obtained from the collected data and used for well-founded asset management decisions.
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