Hybrid institution vacuum circuit breaker action time prediction

Abstract Phase-control technology requires a sufficiently small dispersion of switching operation times, however, even though the switch itself is sufficiently stable, action time dispersion is still a key issue in phase-control technology due to various factors. In this paper, by building a simulation and test model of the hybrid mechanism, the action time variation caused by influencing factors such as fast switching temperature, capacitance voltage, capacitance degradation are investigated, and the test and simulation data are collated and analysed form a database. The Generalized Regression neural network has excellent prediction, self-learning, non-linear mapping and tolerance capabilities, and the prediction error of the GRNN algorithm is verified to be less than 0.2ms for the closing time.