Work

A Kernel Discriminant PCA and contrastive learning–enhanced FCNN framework for UHVDC line fault diagnosis

Year: 2025

Type: article

Abstract: With the rapid development of large-scale clean energy bases in remote regions, ultra-high-voltage direct current (UHVDC) transmission plays a vital role in modern power grids. However, existing line protection schemes exhibit limitations in fault feature representation and utilization, often leading to misoperations under extreme conditions. To tackle these challenges, this study develops a high-precision UHVDC line fault diagnosis framework that synergistically integrates Kernel Discriminant Principal Component Analysis (KDPCA) for enhanced feature separability with a contrastive learning–enhanced Fully Connected Neural Network (CLE-FCNN) for improved robustness and generalization. Physically interpretable zero-mode and line-mode voltage components are first extracted as initial features. KDPCA is then applied to perform nonlinear discriminative feature extraction, thereby enhancing inter-class separability and discriminative capability. Subsequently, the CLE-FCNN is developed to further enhance robustness and generalization by pulling samples of the same class closer together while pushing samples of different classes farther apart in the feature space. Simulation results show that the proposed method can accurately distinguish internal from external faults within a 1 ms window, even under high transition resistances up to 500 Ω . Furthermore, validation on real UHVDC fault samples confirms that the method maintains stable performance under practical operating conditions. • Proposed a UHVDC fault diagnosis framework combining KDPCA with CLE-FCNN. • Developed KDPCA to improve nonlinear feature separability for UHVDC fault data. • Designed CLE-FCNN to boost generalization under diverse operating conditions. • Reached near-perfect accuracy within 1 ms post-fault, enabling ultra-fast protection. • Ensured robustness across fault locations, high resistances, and real UHVDC cases.

Source: Electric Power Systems Research

DOI: 10.1016/j.epsr.2025.112192

Authors: Haojie Zhang, Qingwu Gong

Institution: Wuhan University

Language: English

Topic: Power Systems Fault Detection

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

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

Open Access status: hybrid

Estimated APC paid: $2,810