Improved information and insight for decision support in operations and design are central promises of a smart grid. Well-structured information about the composition of power systems is increasingly becoming available in the domain, e.g. due to standard information models (e.g. CIM or IEC61850) or otherwise structured databases. More measurements and data do not automatically improve decisions, but there is an opportunity to capitalize on this information for decision support. With suitable reasoning strategies data can be contextualized and decision-relevant events can be promoted and identified. This paper presents an approach to link available structured power system data directly to a functional representation suitable for diagnostic reasoning. The translation method is applied to test cases also illustrating decision support.
Proceedings of Iwies 2013, 2013
Power system modeling; Knowledge-based methods; Functional modeling; Model translation; Decision support