Fast Assessment Method for Transmission-Interface TTC Under N−1 Scenarios

In practical power system operation, equipment maintenance and line outages may cause changes in network topology and power flow redistribution. Therefore, fast assessment of the total transfer capability (TTC) of transmission interfaces under N−1 line-outage scenarios is important for operational adjustment and security control. Existing deep-learning-based TTC assessment methods mainly rely on fixed-order vectorized representations, making it difficult to explicitly preserve network connectivity, variations in branch operating states, and regional power transfer characteristics of different target interfaces. To address these issues, this paper proposes a fast multi-interface TTC assessment method based on an edge-feature-enhanced message passing neural network (MPNN). The power system is represented as a directed graph integrating bus operating states, branch electrical states, line in-service states, and target-interface regional information. A line-status-controlled message passing mechanism is combined with branch electrical edge features, enabling node representations to jointly capture effective network connectivity and branch operating states. An interface-aware readout layer is further designed to aggregate system-wide, sending-area, and receiving-area representations and construct interface-specific graph-level features for TTC regression. Case studies on the IEEE 39 bus and IEEE 118 bus systems demonstrate that the proposed model achieves stable TTC prediction across different N−1 topology scenarios and exhibits good adaptability to topology changes.