Energy theft detection for AMI using principal component analysis based reconstructed data

Sandeep Kumar Singh, Ranjan Bose, Anupam Joshi

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

Abstract

To detect energy theft attacks in advanced metering infrastructure (AMI), we propose a detection method based on principal component analysis (PCA) approximation. PCA approximation is introduced by dimensionality reduction of high dimensional AMI data and the authors extract the underlying consumption trends of a consumer that repeat on a daily or weekly basis. AMI data is reconstructed using principal components and used for computing relative entropy. In the proposed method, relative entropy is used to measure the similarity between two probability distributions derived from reconstructed consumption dataset. When energy theft attacks are injected into AMI, the probability distribution of energy consumption will deviate from the historical consumption, so leading to a larger relative entropy. The proposed detection method is tested under different attack scenarios using real-smart-meter data. Test results show that the proposed method can detect theft attacks with high detection percentage.

Original languageEnglish
Pages (from-to)179-185
Number of pages7
JournalIET Cyber-Physical Systems: Theory and Applications
Volume4
Issue number2
DOIs
StatePublished - Jun 1 2019
Externally publishedYes

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