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

An Extreme Learning Machine-Based System Frequency Nadir Constraint Linearization Method