Power Systems Decomposition for Robustifying State Estimation Under Cyber Attacks

This paper proposes an automatic algorithm for the on-line implementation of a robust static state estimator on large power systems. The proposed algorithm maximizes the number of outliers and cyber-attacks that the estimator can resist while giving reliable estimates. The large power system is decomposed in several islands or subsystems, and a highly robust regression estimator, namely the least trimmed squares estimator (LTS), is implemented on each island to detect bad data. Executing the estimators in parallel will greatly reduce the computation time of the robust static state estimator. The introduced method is compared with two cycle detection graph-theory approaches, namely depth-first search (DFS) and minimum spanning tree (MST), which have been adapted here for power state estimation. Simulations on IEEE 14, 30, 57, 118, 145, and 300 bus systems show the superior performance of the proposed algorithm over the adapted DFS and MST. The algorithm could reduce significantly the number and size of cycles in the system. Furthermore, the number of detected outliers, and attacks is maximized while the observability of the system is ensured. Attacks or outliers on both measurements, and topology of the grid are detected as well.