Branch-and-Bound Guided Search for Critical Elements in State Estimation

State estimation (SE) is essentially a filtering process with an avid appetite for data. When destitute of redundant, varied, and well-located measurements, SE suffers the consequences of having a poor capability for grid observation. In that event, SE may produce unreliable results, since bad data detection/identification is performed in a precarious way. In short, the presence of critical data (those whose unavailability makes the grid unobservable), limits the SE performance. The search for critical elements of the SE process-e.g., measurements, considered individually or in groups-is an indispensable, laborious task, which involves solving a problem of combinatorial nature. This paper proposes a methodology for the identification of critical data by means of the branch-and-bound paradigm. Numerical results performed on IEEE benchmark systems demonstrate the potential/practical application of the proposed methodology.