GPU parallel processing to enable extensive criticality analysis in state estimation

Abstract

Power system monitoring relies on the reliability of state estimation (SE) results. SE plays a dominant role in data debugging if sufficient data is available. Criticality analysis (CA) integrates SE as a module in which measurements—taken one-by-one or in groups (tuples) of minimal cardinality—are designated crucial. The combinatorial nature of extensive CA (not restricted to identifying low-cardinality critical tuples) characterizes its computational complexity and imposes challenging limits to go beyond. In simple terms, these limits are established by the number of measurements to be combined, the cardinality of tuples, and the computing time required to check the criticality condition. This paper proposes an innovative computational solution to expand CA limits found to date in the literature. A framework with multi-threads designed cleverly on a graphics processing unit (GPU) parallel processing environment is built. The conceived architecture favors evaluating massive measurement combinations of diverse cardinality in extensive CA. Numerical results reveal significant speed-ups with the proposed approach, contrasting with those reported in research efforts published so far.