6 February 2019
power system security, power engineering computing, principal component analysis, smart power grids, smart meters, security of data, entropy, probability, energy theft detection, reconstructed data, energy theft attacks, advanced metering infrastructure, principal component analysis approximation, PCA approximation, dimensionality reduction, high dimensional AMI data, daily basis, weekly basis, principal components, probability distribution, reconstructed consumption dataset, energy consumption, historical consumption, larger relative entropy, real-smart-meter data, high detection percentage, consumption trends, relative entropy
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.