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

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Jul 8, 2014
Gilbert Cassar Mark Walker Yueting Yang

Abstract

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 616 | PDF Downloads 148

##plugins.themes.bootstrap3.article.details##

Keywords

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

References
Ferrell, B. L. (1999), JSF Prognostics and Health Management. Proceedings of IEEE Aerospace Conference. March 6-13, Big Sky, MO. doi: 10.1109/AERO.1999.793190
Khurana, H., Hadley, M., Lu, N., & Frincke, D. (2010). Smart Grid Security Issues. IEEE Security and Privacy.
Lu, B., Tinker, M., Apon, A., Hoffman, D., & Dowdy, L. (2005). Adaptive automatic grid reconfiguration using workload phase identification. First international conference on e-Science and Grid Computing.
NIST. (2012). NIST Framework and Roadmap for Smart Grid Interoperability Standards.
NIST. (2013). Technology, Measurements, and Standards Challenges for the Smart Grid.
Patterson-Hine, A., Aaseng, G., Biswas, S., Narashimhan, K., Pattipati, K.(2005). A Review of Diagnostic Techniques for ISHM Applications. ISHM Forum 2005, Napa, CA.
Prabhu, R (2013) Smart Grid Receives $434 Million in VC Funding, $17 billion in M & A transactions recorded Retrieved from www.engrreview.com/Editorial _pages/2013/02/ER0213_Client-Tech_22.html
Schwabacher, M., & Goebel, K. F. (2007). A survey of artificial intelligence for prognostics. Proceedings of AAAI Fall Symposium, November 9–11, Arlington, VA. www.aaai.org/Library/Symposia/Fall/2007/fs07-02-016.php
Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering system. Hoboken, NJ: John Wiley & Sons, Inc
Walker, M., (2007). Model-based Reasoning Applications for Remote Intelligent Systems Health Management. Proceedings of ASNE Intelligent Ships Symposium. May 2007.
Walker, M,, Kapadia, R. (2009). Integrated Design of Online Health and Prognostics Management. Proceedings of the Annual PHM Society Conference, San Diego, CA. October 2009.
Walker, M. (2010). Next Generation Prognostics and Health Management for Unmanned Vehicles. Proceedings of the IEEE Aerospace Conference, Big Sky Montana, March 2010.
Section
Technical Papers

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.