Automated Node Configuration via Predictive Analytics in Smart Grids: Separating Matched-Condition Accuracy from Operational Reliability

Abstract

Automated node configuration in smart grids, fault-triggered reconfiguration, tap-changer control, and switching decisions issued without operator confirmation, depends on predictive models validated primarily under structural correspondence between deployment and training topology. Reported precision for these models reaches 94.28% under matched topology and telemetry conditions. Under topology shift, F1-score for graph-based fault detectors decreases by 12% for attention-based architectures and by up to 60% for recurrent architectures without graph structure. A separate class of false data injection attacks, constructed against on-load tap-changer volt/var optimization, steers control decisions toward harmful setpoints without triggering residue-based bad-data detection. Two conditions determine operational reliability for automated node configuration: structural correspondence between deployment and training topology, and integrity of the measurements the model consumes. Reported evaluation practice combines both conditions into a single accuracy or precision figure. A validity-monitoring architecture, built on a distinction between aleatoric uncertainty and epistemic uncertainty, separates the two conditions into a topology-comparison check, evaluated by direct comparison of deployed and trained network structure, and an execution-time feasibility check computed independently of classifier confidence.