Biblio

False data injection attack (FDIA) is a real threat to smart grids due to its wide range of vulnerabilities and impacts. Designing a proper detection scheme for FDIA is the 1stcritical step in defending the attack in smart grids. In this paper, we investigate two main statistical techniques-based approaches in this regard. The first is based on the principal component analysis (PCA), and the second is based on the canonical correlation analysis (CCA). The test cases illustrate a better characterization performance of FDIA using CCA compared to the PCA. Further, CCA provides a better differentiation of FDIA from normal grid contingencies. On the other hand, PCA provides a significantly reduced false alarm rate.