Leveraging Next Generation Reasoning for Prognostics and Health Management of the Smart Grid



Published Jul 8, 2014
Gilbert Cassar Mark Walker Yueting Yang


With the increasing complexity from an evolving Smart Grid, the significance of providing real-time situational awareness and the ability to leverage advanced reasoning and prediction for control and automation will become key differentiators for service providers. Similar techniques are being applied within prognostics and health management (PHM) applications and are providing value by predicting and assuring system reliability, performing real-time detection and diagnosis of failure, and presenting current and predicted system states to users to aid in decision making. With the overlap in application and requirements for advanced software techniques, the smart grid industry is compelled to investigate products and processes applied to PHM across other domains. However, the complexity of grid management, the speed of technology development, the dynamic nature of electric power supply and demand – each of these contribute to the necessity for applying advanced reasoning capabilities that provide more flexibility to developers and users. Such advanced capabilities allow for leveraging all available information, enabling accurate predictions of future conditions and availability, and incorporating the necessary knowledge for making high level decisions. Object oriented, model-based reasoning systems have demonstrated value within the PHM community for handling such complexity, and in this paper the authors discuss a pragmatic approach for applying these next generation PHM techniques to the smart grid.

How to Cite

Cassar, G., Walker, M., & Yang, Y. (2014). Leveraging Next Generation Reasoning for Prognostics and Health Management of the Smart Grid. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1562
Abstract 562 | PDF Downloads 130



diagnostics, Intelligent Health Monitoring, model based reasoning, smart grid, surveillance

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