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

TL;DR

This paper introduces an ELM-based approach to linearize the frequency nadir constraint in power system scheduling, improving accuracy and computational efficiency for integrating renewable energy sources.

Contribution

It proposes a novel ELM-based method that combines two-step fitting into one training process for better linearization of the frequency nadir constraint.

Findings

Superior fitting accuracy demonstrated in simulations

Reduces complexity of nonlinear FNC in scheduling

Enhances integration of renewable energy sources

Abstract

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.