Wide-Frequency Oscillatory Source Localization of a New Power System Based on Transformer-GAT
Year: 2025
Type: article
Abstract: [Objective] To solve the problem of new power system security caused by large-scale new-energy-grid connections from traditional industrial frequency bands to medium- and high-frequency bands， and to promote systematic research on a broadband oscillation source location method based on artificial intelligence， a wide-frequency oscillation source location method based on a transformer and graph attention neural network （GAT） is proposed. [Methods] First， on the substation side， the powerful signal processing and feature extraction capabilities of the transformer network were used to efficiently encode and compress the measurement signals of the power system to ensure the effective transmission of key broadband oscillation information under limited bandwidth conditions and reduce data redundancy. Subsequently， on the main-station side， combined with the compressed signal characteristics and system network topology， the GAT was used to locate the oscillation source. Finally， a four-machine， two-area system with a wind farm was used for verification. [Results] Simulation and experimental results show that the proposed transformer encoder can extract effective features from wide-frequency oscillation signals to realize the compression of substation signals. The proposed GAT model can achieve the accurate positioning of a wide-frequency oscillation source. In the comparison experiments with other algorithms， the GAT model had a lower false alarm cost when locating the oscillation source and maintained a balance between sensitivity and specificity. [Conclusions] Through the collaborative analysis of signal characteristics and network topology， the Transformer-GAT method effectively improves the positioning accuracy and robustness of wide-frequency oscillation sources. It provides technical support for the stable operation of new power systems.
Author: Qingyuan ZHANG, Bo ZHOU, Jianfei CHI, Yan ZHAO, Liang TAO, Xiao HU
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7084082220
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