A Multi-layer, Hierarchical Information Management System for the Smart Grid
Abstract
This paper presents the modeling approach, methodologies, and initial results of setting up a multi-layer, hierarchical information management system (IMS) for the smart grid. The IMS allows its users to analyze the data collected by multiple control and communication networks to characterize the states of the smart grid. Abnormal, corrupted, or erroneous measurement data and outliers are detected and analyzed to identify whether they are caused by random equipment failures, unintentional human errors, or deliberate tempering attempts. Data collected from different information networks are crosschecked for data integrity based on redundancy, dependency, correlation, or cross-correlations, which reveal the interdependency between data sets. A hierarchically structured reasoning mechanism is used to rank possible causes of an event to aid the system operators to proactively respond or provide mitigation recommendations to remove or neutralize the threats. The model provides satisfactory performance on identifying the cause of an event and significantly reduces the need of processing myriads of data collected.
- Authors:
- Publication Date:
- Research Org.:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1028555
- Report Number(s):
- PNNL-SA-77218
TD5018010; TRN: US201122%%396
- DOE Contract Number:
- AC05-76RL01830
- Resource Type:
- Conference
- Resource Relation:
- Conference: Proceedings of the 2011 IEEE Power and Energy Society General Meeting, July 24-29, 2011, Detroit, Michigan
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; COMMUNICATIONS; MANAGEMENT; MITIGATION; PERFORMANCE; PROCESSING; RECOMMENDATIONS; REDUNDANCY; SIMULATION; TEMPERING; cyber security; reliability; smart grid; predictive defense; interoperability; data integrity
Citation Formats
Lu, Ning, Du, Pengwei, Paulson, Patrick R, Greitzer, Frank L, Guo, Xinxin, and Hadley, Mark D. A Multi-layer, Hierarchical Information Management System for the Smart Grid. United States: N. p., 2011.
Web. doi:10.1109/PES.2011.6039214.
Lu, Ning, Du, Pengwei, Paulson, Patrick R, Greitzer, Frank L, Guo, Xinxin, & Hadley, Mark D. A Multi-layer, Hierarchical Information Management System for the Smart Grid. United States. https://doi.org/10.1109/PES.2011.6039214
Lu, Ning, Du, Pengwei, Paulson, Patrick R, Greitzer, Frank L, Guo, Xinxin, and Hadley, Mark D. 2011.
"A Multi-layer, Hierarchical Information Management System for the Smart Grid". United States. https://doi.org/10.1109/PES.2011.6039214.
@article{osti_1028555,
title = {A Multi-layer, Hierarchical Information Management System for the Smart Grid},
author = {Lu, Ning and Du, Pengwei and Paulson, Patrick R and Greitzer, Frank L and Guo, Xinxin and Hadley, Mark D},
abstractNote = {This paper presents the modeling approach, methodologies, and initial results of setting up a multi-layer, hierarchical information management system (IMS) for the smart grid. The IMS allows its users to analyze the data collected by multiple control and communication networks to characterize the states of the smart grid. Abnormal, corrupted, or erroneous measurement data and outliers are detected and analyzed to identify whether they are caused by random equipment failures, unintentional human errors, or deliberate tempering attempts. Data collected from different information networks are crosschecked for data integrity based on redundancy, dependency, correlation, or cross-correlations, which reveal the interdependency between data sets. A hierarchically structured reasoning mechanism is used to rank possible causes of an event to aid the system operators to proactively respond or provide mitigation recommendations to remove or neutralize the threats. The model provides satisfactory performance on identifying the cause of an event and significantly reduces the need of processing myriads of data collected.},
doi = {10.1109/PES.2011.6039214},
url = {https://www.osti.gov/biblio/1028555},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Oct 10 00:00:00 EDT 2011},
month = {Mon Oct 10 00:00:00 EDT 2011}
}