Home > Research > Publications & Outputs > Power consumption profiling using energy time-f...

Electronic data

  • ieee_comm_letters

    Rights statement: ©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Accepted author manuscript, 512 KB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

Links

Text available via DOI:

View graph of relations

Power consumption profiling using energy time-frequency distributions in smart grids

Research output: Contribution to journalJournal articlepeer-review

Published

Standard

Power consumption profiling using energy time-frequency distributions in smart grids. / Marnerides, Angelos; Schaeffer-Filho, Alberto; Smith, Paul; Mauthe, Andreas.

In: IEEE Communications Letters, Vol. 19, No. 1, 01.2015, p. 46-49.

Research output: Contribution to journalJournal articlepeer-review

Harvard

APA

Vancouver

Author

Bibtex

@article{3f028e6edbde4d06b61660f5acd9a5f7,
title = "Power consumption profiling using energy time-frequency distributions in smart grids",
abstract = "Smart grids are power distribution networks that include a significant communication infrastructure, which is used to collect usage data and monitor the operational status of the grid. As a consequence of this additional infrastructure, power networks are at an increased risk of cyber-attacks. In this letter, we address the problem of detecting and attributing anomalies that occur in the sub-meter power consumption measurements of a smart grid, which could be indicative of malicious behavior. We achieve this by clustering a set of statistical features of power measurements that are determined using the Smoothed Pseudo Wigner Ville (SPWV) energy Time-Frequency (TF) distribution. We show how this approach is able to more accurately distinguish clusters of energy consumption than simply using raw power measurements. Our ultimate goal is to apply the principles of profiling power consumption measurements as part of an enhanced anomaly detection system for smart grids.",
author = "Angelos Marnerides and Alberto Schaeffer-Filho and Paul Smith and Andreas Mauthe",
note = "{\textcopyright}2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2015",
month = jan,
doi = "10.1109/LCOMM.2014.2371035",
language = "English",
volume = "19",
pages = "46--49",
journal = "IEEE Communications Letters",
issn = "1089-7798",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - Power consumption profiling using energy time-frequency distributions in smart grids

AU - Marnerides, Angelos

AU - Schaeffer-Filho, Alberto

AU - Smith, Paul

AU - Mauthe, Andreas

N1 - ©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2015/1

Y1 - 2015/1

N2 - Smart grids are power distribution networks that include a significant communication infrastructure, which is used to collect usage data and monitor the operational status of the grid. As a consequence of this additional infrastructure, power networks are at an increased risk of cyber-attacks. In this letter, we address the problem of detecting and attributing anomalies that occur in the sub-meter power consumption measurements of a smart grid, which could be indicative of malicious behavior. We achieve this by clustering a set of statistical features of power measurements that are determined using the Smoothed Pseudo Wigner Ville (SPWV) energy Time-Frequency (TF) distribution. We show how this approach is able to more accurately distinguish clusters of energy consumption than simply using raw power measurements. Our ultimate goal is to apply the principles of profiling power consumption measurements as part of an enhanced anomaly detection system for smart grids.

AB - Smart grids are power distribution networks that include a significant communication infrastructure, which is used to collect usage data and monitor the operational status of the grid. As a consequence of this additional infrastructure, power networks are at an increased risk of cyber-attacks. In this letter, we address the problem of detecting and attributing anomalies that occur in the sub-meter power consumption measurements of a smart grid, which could be indicative of malicious behavior. We achieve this by clustering a set of statistical features of power measurements that are determined using the Smoothed Pseudo Wigner Ville (SPWV) energy Time-Frequency (TF) distribution. We show how this approach is able to more accurately distinguish clusters of energy consumption than simply using raw power measurements. Our ultimate goal is to apply the principles of profiling power consumption measurements as part of an enhanced anomaly detection system for smart grids.

U2 - 10.1109/LCOMM.2014.2371035

DO - 10.1109/LCOMM.2014.2371035

M3 - Journal article

VL - 19

SP - 46

EP - 49

JO - IEEE Communications Letters

JF - IEEE Communications Letters

SN - 1089-7798

IS - 1

ER -