arXiv:2602.11063v3 Announce Type: replace Abstract: Frequency-constrained optimal power flow (FCOPF) should keep the rate of change of frequency (RoCoF) and the frequency deviation (FD) within their limits after a specified generator out-age. The linearized frequency constraints commonly used in FCOPF are derived from aggregated system frequency response (SFR) models, which describe the center of inertia (COI) fre-quency with simplified governor dynamics and can underesti-mate the frequency excursions experienced by individual genera-tors. To address this issue, this paper develops a neural network-based frequency predictor (NN-FP) trained on electromagnetic transient (EMT) simulations in PSCAD/EMTDC. The NN-FP predicts the worst RoCoF and the worst FD over the monitored generators, namely the generators at all buses other than the contingency bus. The trained NN-FP is reformulated and embed-ded into FCOPF with tightened frequency limits obtained from the test set prediction errors of NN-FP, leading to the proposed NN-FCOPF. The proposed model is compared with a traditional optimal power flow and a linearized FCOPF. EMT validation of 24-hour dispatches on the IEEE 9-bus and 39-bus systems shows that NN-FCOPF satisfies both limits in all hourly cases for the specified contingency, whereas the linearized FCOPF violates the limits in some hours because it underestimates the worst genera-tor RoCoF and FD.