Work

Data-Driven Risk Identification Method for Low-Frequency Oscillations in New Power Systems

Year: 2026

Type: article

Abstract: With the increasing scale of new energy grid integration, the risk of low-frequency oscillation in the power system has increased, which seriously affect system safety and stability. It is urgent to identify the risk of low-frequency oscillations through steady-state operating features. This article first analyzes the features that affect low-frequency oscillations and constructs a low-frequency oscillation dataset using transient simulations. Secondly, feature selection was performed using the random forest algorithm, and a low-frequency oscillation risk identification model for GA-CNN was proposed. Thirdly, by combining Pearson correlation coefficient and RF algorithm to eliminate redundant features and screen important features, a low-frequency oscillation frequency recognition model based on GBRT was proposed, and hyperparameter optimization was performed using grid search. Finally, the effectiveness of the proposed method was verified by ablation experiments and comparative experiments using low-frequency oscillation datasets under different operating conditions.

Source: Energies

DOI: 10.3390/en19122899

Authors: Chunhua Li, Yanhong Ma, Bo Wei, Jiexiang Han, Xinyu Guan, Wenying Liu

Institutions: State Grid Corporation of China (China), North China Electric Power University, State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources

Language: English

Topic: Power System Optimization and Stability

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137