research-article

A prediction model for anomalies in smart grid with sensor network

Authors:

Shubhalaxmi Kher,

Dipankar Dasgupta,

Paul MixonAuthors Info & Claims

CSIIRW '13: Proceedings of the Eighth Annual Cyber Security and Information Intelligence Research Workshop

Article No.: 60, Pages 1 - 4

https://doi.org/10.1145/2459976.2460043

Published: 08 January 2013 Publication History

Abstract

A machine learning based model to monitor the smart grid for any suspicious activity or malicious attack is presented in this paper. The model is designed to detect and classify anomalies in the sensory data and is helpful in ensuring the security and stability of the smart grid. The model relies on the real time data collected using wireless sensor networks as an overlay network on the power distribution grid. The overlay network of wireless sensors/devices uses a cluster topology at each tower to collect local information about the tower, and is further augmented by the linear chain topology to connect each tower to the base station (usually at the substation). Preliminary results show that detection mechanism is promising and is able to detect the occurrence of any anomalous event that may cause threat to the smart grid.

References

[1]

U. Sulayman, T. Alouni, Smart grid monitoring using local area sensor network. Real-time data acquisition, analysis and management, Proceedings of the IEEE Southeastern 2011 conference, pp. 444--449.

[2]

S. L. Ullo, A. Vaccaro, G. Velotto. Performance Analysis of IEEE 802.15.4 based Sensor Networks for Smart Grids Communications, Journal of Electrical Engineering: Theory and Application (Vol.1-2010/Iss.3), pp. 129--134.

[3]

Smart Grid Sensing, Monitoring and Control Systems: Market Opportunities 2011, Nano-239, March 2011.

[4]

Chris Karlof, Naveen Sastry, David Wagner, TinySec: Link Layer Security Architecture for Wireless Sensor Networks, SenSys'04, Nov. 3--5, 2004, Baltimore, USA.

Digital Library

[5]

R. Ambikairajah, B. T. Phung, J. Ravishankar, T. R. Blackburn and Z. Liu, Smart Sensors and Online Condition Monitoring of High Voltage Cables for the Smart Grid, In Proceedings of the 14th International Middle East Power Systems Conference (MEPCON), Egypt, December 19-21, 2010, Paper ID 289, pp. 807--811.

[6]

Altea Power monitoring Solutions: http://www.alteasolutions.eu/index.php?option=com_content&view=article&id=52%3Anewsflash3&catid=3%3Anewsflash&Itemid=55&lang=en

[7]

Three-Phase Capacitor Bank Control Enables Smart Grid Initiatives, Schweitzer Engineering Laboratories, Aug 10, 2010. https://www.selinc.com/products/meteringNews.aspx?id=8069.

[8]

M. S. A. Hejazi, J. Ebrahimi, G. B. Gharehpetian, M. Mohammadi,.R. F.-Dana, and G. Moradi. Application of Ultra-Wideband Sensors for On-Line Monitoring of Transformer Winding Radial Deformations--A Feasibility Study, IEEE SENSORS JOURNAL, VOL. 12, NO. 6, pp. 1649--1659, JUNE 2012.

[9]

Y. Yang, R. Harley, D. Divan, and T. Habetler, Adaptive Echo State Network to maximize overhead power line dynamic thermal rating, Proceedings of the IEEE Energy Conversion Congress and Exposition (ECCE), pp. 2247--2254.

[10]

Judy M. Hartley, Wi-Fi and WiMAX: Protocols of Security, December 2007, Intel Corporation.

[11]

Siemens Smart Grid solutions: http://w3.usa.siemens.com/smartgrid/us/en/pages/smartgrid.aspx

[12]

At and T and Smart grid research: http://www.research.att.com/projects/SmartGrid/index.html?fbid =nFzQQskFfOw.

[13]

Greentechgrid, IBM, Cisco Look to Tie Up Smart Grid Partners, Sep 17, 2009.

[14]

W. Wang, Y. Xu, and M. Khanna, A survey on the communication architectures in smart grid, Computer Networks, vol. 55, pp. 3604--3629, 2011.

Digital Library

[15]

The MEMSIC solution: Wireless sensor networks. http://www.memsic.com/technology/wireless-sensor-networks.html

[16]

WEKA: A machine learning tool set. Software downloadable from: http://www.cs.waikato.ac.nz/ml/weka/index_downloading.html

Cited By

Panda M(2017)Intelligent data analysis for sustainable smart grids using hybrid classification by genetic algorithm based discretizationIntelligent Decision Technologies10.3233/IDT-17028311:2(137-151)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.3233/IDT-170283
Pinto AScioscia FLoseto GRuta MBove EDi Sciascio E(2015)A semantic-based approach for Machine Learning data analysisProceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)10.1109/ICOSC.2015.7050828(324-327)Online publication date: Feb-2015
https://doi.org/10.1109/ICOSC.2015.7050828
Ding YNeumann MDa Silva PBeigl M(2013)A framework for short-term activity-aware load forecastingJoint Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments and Workshop on Semantic Cities10.1145/2516911.2516919(23-28)Online publication date: 4-Aug-2013
https://dl.acm.org/doi/10.1145/2516911.2516919

Index Terms

A prediction model for anomalies in smart grid with sensor network

Recommendations

Security of wireless sensor network (WSN) in smart grid
ICC '17: Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing

A smart grid is a well-thought-out smart network of meta-systems and subsystems that aims at improving the efficiency the traditional power grid and at ensuring reliable energy delivery. To achieve its goals, a smart grid requires a two-way ...
Software models for smart grid
SE4SG '12: Proceedings of the First International Workshop on Software Engineering Challenges for the Smart Grid

Smart grid technology is progressing worldwide. Various Countries are investing to transform their traditional power grid to Smart grid. They have started realigning their organization to support a Smart grid vision. At this initial stage some software ...
Elliptical anomalies in wireless sensor networks

Anomalies in wireless sensor networks can occur due to malicious attacks, faulty sensors, changes in the observed external phenomena, or errors in communication. Defining and detecting these interesting events in energy-constrained situations is an ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

CSIIRW '13: Proceedings of the Eighth Annual Cyber Security and Information Intelligence Research Workshop

January 2013

282 pages

ISBN:9781450316873

DOI:10.1145/2459976

Editors:
Frederick Sheldon
Oak Ridge National Laboratory
,
Annarita Giani
Los Alamos National Laboratory
,
Axel Krings
University of Idaho
,
Robert Abercrombie
Oak Ridge National Laboratory

Copyright © 2013 Authors.

Sponsors

Los Alamos National Labs: Los Alamos National Labs
Sandia National Labs: Sandia National Laboratories
DOE: Department of Energy
Oak Ridge National Laboratory
Lawrence Livermore National Lab.: Lawrence Livermore National Laboratory
BERKELEYLAB: Lawrence National Berkeley Laboratory
Argonne Natl Lab: Argonne National Lab
Idaho National Lab.: Idaho National Laboratory
Pacific Northwest National Laboratory
Nevada National Security Site: Nevada National Security Site

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 January 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Conference

CSIIRW '13

Sponsor:

Los Alamos National Labs
Sandia National Labs
DOE
Lawrence Livermore National Lab.
BERKELEYLAB
Argonne Natl Lab
Idaho National Lab.
Nevada National Security Site

CSIIRW '13: Cyber Security and Information Intelligence

January 8 - 10, 2013

Tennessee, Oak Ridge, USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
229
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Panda M(2017)Intelligent data analysis for sustainable smart grids using hybrid classification by genetic algorithm based discretizationIntelligent Decision Technologies10.3233/IDT-17028311:2(137-151)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.3233/IDT-170283
Pinto AScioscia FLoseto GRuta MBove EDi Sciascio E(2015)A semantic-based approach for Machine Learning data analysisProceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)10.1109/ICOSC.2015.7050828(324-327)Online publication date: Feb-2015
https://doi.org/10.1109/ICOSC.2015.7050828
Ding YNeumann MDa Silva PBeigl M(2013)A framework for short-term activity-aware load forecastingJoint Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments and Workshop on Semantic Cities10.1145/2516911.2516919(23-28)Online publication date: 4-Aug-2013
https://dl.acm.org/doi/10.1145/2516911.2516919

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten