False Data Injection Attack With Minimal Network Information

State estimation algorithms are essential for assessing the operational states of modern power grids. This study investigates unobservable false data injection attacks using limited topology and parameter information of the grid. A nuclear norm minimization-based matrix completion approach (MCA), combined with the exploration of its null space, has been undertaken to demonstrate stealthy attack vectors (AVs) against the nonlinear state estimator under renewable uncertainty that can bypass the conventional residue test-based bad data detectors (BDDs), even in noisy conditions and under varying numbers of attacked state variables (NASVs). The effectiveness of this method is validated on the IEEE 118 bus test system.