@article{ffa6037d0e5c490187076b350dc27c5a,
title = "Designing Digitally Enabled Proactive Maintenance Systems in Power Distribution Grids: A Scoping Literature Review",
abstract = "The digitalization of the power distribution grid has surged over the past decade. This transformation has given rise to a host of new data-driven applications focused on condition monitoring and predictive maintenance. However, from the perspective of the distribution system operator, there remains uncertainty about what and how digital maintenance processes can be realized. Additionally, the lack of clarity regarding the relative payback of investments makes it difficult to plan investments in digital maintenance systems optimally. The existing literature does not provide a holistic investigation of proactive maintenance applications, specifically the interplay between how data selection, usage, and degree of digitalization impacts the development of proactive maintenance applications. In this paper, we therefore study the chain of design choices linked to the development of proactive maintenance systems, through a scoping review of existing approaches. Thereby, we offer a valuable resource for power distribution system operators in guiding their decision-making and implementation processes. Additionally, it enables us to point out gaps in existing literature, which can inform future studies. Furthermore, we provide an extendable Sankey diagram-based visualization tool, which enables researchers and practitioners alike to further investigate the complex relationships between proactive maintenance design choices. Eventually, we propose a conceptual model for power distribution systems operators to better understand the benefits of digitally enabled proactive maintenance systems, which can aid investment decision-making.",
keywords = "scoping review, distribution grids, asset management, digitalization, condition monitoring, predictive maintenance, components, data, machine learning, Digitalization, Data, Asset management, Predictive maintenance, Components, Condition monitoring, Machine learning, Scoping review, Distribution grids",
author = "Mortensen, {Lasse Kappel} and Konrad Sundsgaard and Shaker, {Hamid Reza} and Hansen, {Jens Zo{\"e}ga} and Guangya Yang",
year = "2024",
month = dec,
doi = "10.1016/j.egyr.2024.08.044",
language = "English",
volume = "12",
journal = "Energy Reports",
issn = "2352-4847",
publisher = "Elsevier",
}