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The modernisation of medium voltage cable systems must be prioritised effectively. Fortunately, there exist methods which can rank cables by health and thus schedule investments properly. Thereby, many approaches benefit greatly from historical failure data. However, if available, failure records rarely include all required features, such as cable age or location. Consequently, strategies for collecting these characteristics are needed for modelling success. This study uses the Danish database for distribution grid failures as a case study to elaborate on effective approaches to obtain more complete raw failure records. After highlighting missing information in the available data, several data collection and fusion strategies are applied, including joins with further asset registers from distribution system operators.
Failure data, medium voltage cables, reliability, asset management, machine learning, data fusion, Failure data, medium voltage cables, reliability, asset management, machine learning, data fusion
Failure data, medium voltage cables, reliability, asset management, machine learning, data fusion, Failure data, medium voltage cables, reliability, asset management, machine learning, data fusion
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