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Marnerides, A, Smith, P, Schaeffer, A and Mauthe, A (2014) Power Consumption Profiling Using Energy Time-Frequency Distributions in Smart Grids. IEEE Communications Letters, 19 (1). pp. 46-49. ISSN 1089-7798
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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.
| Item Type: | Article |
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| Additional Information: | (c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works |
| Uncontrolled Keywords: | 0906 Electrical And Electronic Engineering, 1005 Communications Technologies, 0805 Distributed Computing |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) |
| Divisions: | Computer Science & Mathematics |
| Publisher: | IEEE |
| Date Deposited: | 16 Jan 2015 15:11 |
| Last Modified: | 04 Sep 2021 14:45 |
| DOI or ID number: | 10.1109/LCOMM.2014.2371035 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/312 |
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