A power grid topology adaptive system and method based on digital twinning

Abstract

The application provides a power grid topology adaptive system and method based on digital twinning, comprising: acquiring power grid operation condition information by collecting real-time data, processing frequent fluctuation signals by using a particle swarm optimization algorithm to obtain a stabilized condition characteristic vector; extracting resource redistribution parameters from the determined node connection relationship, simulating an overload phenomenon scenario by using a deep reinforcement learning model to obtain a preliminary distribution scheme; updating the power grid topology structure through the stability result of the optimized scheme, acquiring new power flow path data, and determining the whole network resource matching relationship; executing output adjustment according to the determined whole network resource matching relationship, refining the generator unit distribution by using the particle swarm optimization algorithm to obtain balanced output distribution; verifying the constraint satisfaction degree from the obtained balanced output distribution, if all hard constraints are satisfied, applying to actual power grid regulation and control to obtain a dynamically adaptive operation state.

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