DT-ADC-FedAvg: application of a communication delay-compensated asynchronous federated learning framework in coordinated AVC for main and distribution networks

Federated learning-based automatic voltage control (AVC) in coordinated main–distribution networks is challenged by dynamic topologies, heterogeneous data, communication delays, and client dropout. This paper proposes DT-ADC-FedAvg, a dynamic topology-aware and delay-compensated asynchronous federated learning framework for collaborative AVC optimisation. The method adaptively handles client participation variations, compensates stale asynchronous updates, and integrates robust aggregation with drift detection to mitigate data heterogeneity and network churn. Experiments on three real-world and synthetic power system datasets show that DT-ADC-FedAvg achieves faster convergence, higher control accuracy, and stronger robustness against delays and dropout compared with state-of-the-art federated baselines.