Response-Driven Online Emergency Control for Power System Transient Stability via ConvLSTM-Based sBTTC Sensitivity Prediction

With increasing renewable and power-electronic penetration, emergency control must convert early post-fault measurements into feasible actions within a short latency budget. This paper proposes a response-driven online framework that uses the simplified branch transient transmission capacity (sBTTC) as a physically interpretable interface between causal stability forecasting, action-response prediction, and constrained control. A preceding masked Informer forecasts the no-control all-branch sBTTC trajectories from the first 1.00 s of measured response; a ConvLSTM then predicts generator-tripping recovery and the recovery associated with four load-shedding levels. These predictions are embedded in a weighted mixed-integer piecewise-linear model with stability-recovery, action-bound, and power-balance constraints. On the studied 100-bus renewable-rich AC/DC system, ConvLSTM achieved an RMSE of 7.193×10−3 and an MAE of 5.674×10−3 with 69.16 ms inference time. Its inference was 66.42% faster than Informer, while its RMSE was only 2.06% higher; relative to conventional LSTM, its RMSE and MAE were reduced by 28.21% and 21.53%, respectively. Across 62 grouped out-of-sample disturbances, the validation results give a 96.77% control success rate. In the representative disturbance, 900 MW of generation tripping and 740 MW of load shedding restored the nonlinear terminal sBTTC to 0.998, and the command was issued 1.36 s after fault inception. The framework provides an auditable forecast–response–decision chain; its additive approximation is restricted to the validated action range and uses an empirical 0.05 sBTTC recovery margin selected to exceed the observed 95th-percentile absolute error; this margin is not interpreted as a worst-case error bound.