Tactics for improving computational performance of criticality analysis in state estimation

Abstract Power system state estimation is a needful tool for providing reliable data on the system operating conditions. As such, state estimation requires adequate data redundancy levels, which can be expressed in terms of the degrees of criticalities associated with different groups of measurements. Criticality analysis allows the assessment of the potential risks that could impact state estimation results. The identification of criticalities is a computationally‐intensive process owing to the combinatorial nature of the problem. This paper proposes some Branch‐and‐Bound‐based tactics geared up for improving the computational efficiency of criticality analysis in state estimation. Throughout the paper, the authors sum up significant advances concerning their previous research, in terms of branching and bounding operations, choice of search strategies, data structures adopted, the proposition of a coefficient of criticality, uninformed and informed search schemes, different ways of objective function evaluation, and exploration of different objectives to be accomplished. Simulation results obtained on the IEEE 118‐bus test system evince the performance of the proposed algorithms.