Online Characterization and Detection of False Data Injection Attacks in Wide-Area Monitoring Systems

False data injection attack (FDIA) is a major threat in wide-area monitoring systems. Being able to differentiate FDIA from normal grid contingencies is a paramount necessity for a grid operator to decide the correct response on a critical prompt basis as well as reduce the overall FDIA's false alarms. Two FDIA's characterization algorithms are developed in this paper. The first is based on the principal component analysis (PCA) while the second is based on the canonical correlation analysis (CCA). Both algorithms are developed in an online platform to reduce the computational complexity. The various designed test cases demonstrate a promising FDIA characterization performance utilizing both algorithms. The testing results of three machine learning-based classifiers indicate that the proposed FDIA's characterization algorithms provide better classification models than conventional PCA-based characterization algorithm with CCA illustrating advanced characterization and detection results.