DocumentCode :
2055641
Title :
A multi-layer, data-driven advanced reasoning tool for intelligent data mining and analysis for smart grids
Author :
Ning Lu ; Pengwei Du ; Greitzer, F.L. ; Guo, X. ; Hohimer, R.E. ; Pomiak, Y.G.
Author_Institution :
Pacific Northwest Nat. Lab., Richland, WA, USA
fYear :
2012
fDate :
22-26 July 2012
Firstpage :
1
Lastpage :
7
Abstract :
This paper presents the multi-layer, data-driven advanced reasoning tool (M-DART), a proof-of-principle decision support tool for improved power system operation. M-DART will cross-correlate and examine different data sources to assess anomalies, infer root causes, and anneal data into actionable information. By performing higher-level reasoning “triage” of diverse data sources, M-DART focuses on early detection of emerging power system events and identifies highest priority actions for the human decision maker. M-DART represents a significant advancement over today´s grid monitoring technologies that apply offline analyses to derive model-based guidelines for online real-time operations and use isolated data processing mechanisms focusing on individual data domains. The development of the M-DART will bridge these gaps by reasoning about results obtained from multiple data sources that are enabled by the smart grid infrastructure. This hybrid approach integrates a knowledge base that is trained offline but tuned online to capture model-based relationships while revealing complex causal relationships among data from different domains.
Keywords :
data mining; decision making; power engineering computing; smart power grids; M-DART; diverse data sources; grid monitoring technologies; higher-level reasoning triage; human decision maker; improved power system operation; individual data domains; intelligent data mining; isolated data processing mechanisms; model-based guidelines; model-based relationships; multilayer data-driven advanced reasoning tool; multiple data sources; online real-time operations; proof-of-principle decision support tool; smart grid infrastructure; Cognition; Decision making; Energy consumption; Humans; Monitoring; Smart grids; advanced metering infrastructure; data mining; data-driven models; distribution; energy management systems; information management; meter data management; smart alarm; smart grid; smart meter;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Society General Meeting, 2012 IEEE
Conference_Location :
San Diego, CA
ISSN :
1944-9925
Print_ISBN :
978-1-4673-2727-5
Electronic_ISBN :
1944-9925
Type :
conf
DOI :
10.1109/PESGM.2012.6345180
Filename :
6345180
Link To Document :
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