Abstract
While smart grid technologies are deployed to help achieve improved grid reliability and efficiency, energy companies are vulnerable to cyber-attacks leading to billions of dollars loss. The selection of an appropriate classification method for the electricity theft detection relies on operational requirements and resource constraints in real scenarios. Since unsupervised methods have high error rates, we employ a semi-supervised anomaly detection method [the principal component analysis (PCA)] technique for the electricity theft detection. PCA is compared with the peer-to-peer (P2P) method based on linear equations. The P2P method assumes known particular electricity theft patterns and in the absence of which, the P2P method detection system results in 100% false alarm. While PCA does not require any prior assumptions about the pattern of the electricity theft, 4% false alarm rate is observed. Our analysis shows an average of 45% improvement in the detection accuracy rate in comparison with the P2P method.
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