Assessing strengths and weaknesses of measurement sets for state estimation

Real-time data redundancy is the main determinant of the state estimation (SE) performance, assessed considering the quantity, type, and location of measurements. Redundancy is a generic term used to given a rough idea of the strengths and weaknesses of measurement sets for SE. Data validation routines have a propensity to fail, when spurious measurements take place and inadequate redundancy levels are reached. These levels are associated with the presence of critical data, i.e. those whose unavailability makes the grid unobservable. The search for critical data is a vital, arduous task, which involves solving a problem of combinatorial nature. This paper proposes a method for the identification of critical data through the processing of residual covariance matrices. The criticality of measurements and measuring units (RTUs and PMUs) are evaluated. Numerical results performed on the IEEE 14-bus test system demonstrate the potential/practical application of the proposed methodology.