A Risk Diagnosis Framework for Power Grids Based on Spatial-Temporal Recurrent Graph Neural Network
Risk diagnostics are useful for clarifying risk isolation scenarios and specifying system recovery actions,which can provide decision-making reference for the arrangement in the maintenance mode of power grids.With the increasing of distributed energy integration,the inverter-based generator has less abnormal current and the traditional relay protection fails,which raises new requirements for the risk perception of power grid systems.A risk diagnosis framework based on Spatial-Temporal Recurrent Graph Neural Network(STRGNN)for power grids is proposed,which improves the risk identification ability of maintenance methods.STRGNN can extract temporal and spatial features from the voltage measurement unit data on the key bus.Based on these features,risk event detection,risk type/phase classification,risk localization,and other operations are performed.Compared with previous research results,STRGNN has better generalization ability for risk diagnosis.In addition,STRGNN extracts voltage signals rather than current signals,so there is no need to install relays on all lines of the grid system and STRGNN is not constrained by the number of current measuring units.Extensive experiments on the Potsdam Microgrid System and IEEE-123 node feeder system show that STRGNN has better performance than other benchmark methods.When compared with the most advanced graph convolution method,the accuracy of risk location has increased by 1.8％on the IEEE-123 node feeder systems.
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