  Large-scale integration of converter-based renewable energy sources (RESs)
into the power system will lead to a higher risk of frequency nadir limit
violation and even frequency instability after the large power disturbance.
Therefore, it is essential to consider the frequency nadir constraint (FNC) in
power system scheduling. Nevertheless, the FNC is highly nonlinear and
non-convex. The state-of-the-art method to simplify the constraint is to
construct a low-order frequency response model at first, and then linearize the
frequency nadir equation. In this letter, an extreme learning machine
(ELM)-based network is built to de-rive the linear formulation of FNC, where
the two-step fitting process is integrated into one training process and more
details about the physical model of the generator are considered to reduce the
fitting error. Simulation results show the superiority of the proposed method
on the fitting accuracy.
