GPU-Based Criticality Analysis Applied to Power System State Estimation

da Silva Junior, Ayres Nishio; Clua, Esteban W. G.; Do Coutto Filho, Milton B.; de Souza, Julio C. Stacchini

State Estimation (SE) is one of the main tools in the operation of power systems. Its primary role is to filter statistically small errors and to eliminate spurious measurements. SE requires an adequate number of measurements, varied, and strategically distributed in the power grid. Criticality analysis consists of identifying which combinations of measurements, forming tuples of different cardinalities, are essential for observing the power network as a whole. If a tuple of measurements becomes unavailable and, consequently, unobservable, the tuple is considered critical. The observability condition is verified by the factorization of the residual covariance matrix, which usually is a time-consuming computation task. Also, the search for criticalities is costly, being a combinatorial based problem. This paper proposes a parallel approach of the criticality analysis in the SE realm, through multi-threads execution on CPU and GPU environments. To date, no publication reporting the use of GPU for computing critical elements of the SE process is found in the specialized literature. Numerical results from simulations performed on the IEEE 14- and 30-bus test systems showed speed-ups close to 25[Formula: see text] , when compared with parallel CPU architectures .