Work

Flexible DC transmission lines protection method based on StarNet

Year: 2026

Type: article

Abstract: To address the challenges of threshold selection in existing fault diagnosis methods for flexible DC transmission lines, this study proposes a fault identification approach integrating gramian angular field (GAF) and StarNet-based analysis. The methodology initiates by transforming sequential voltage data under diverse fault conditions into two-dimensional voltage GAF images through GAF-based encoding. These generated voltage GAF images are subsequently partitioned into training and testing datasets to facilitate the training and validation processes of the StarNet. During model development, the network architecture is optimized through partial convolutional layers, while convergence efficiency is enhanced via a cosine annealing algorithm. Comprehensive simulations evaluating transition resistance, noise interference, sampling frequency, and lightning strike disturbances demonstrate the proposed method’s adaptability and superior diagnostic performance across multiple operational scenarios.

Source: International Journal of Electrical Power & Energy Systems

DOI: 10.1016/j.ijepes.2026.111580

Authors: Yanfang Wei, Junjiang Wang, Zhanye Yang, Zhihui Zeng, Xiaowei Wang, Jiayin Li

Institutions: Henan Polytechnic University, Xi'an University of Technology

Language: English

Topic: Power Systems Fault Detection

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,920