Citation :

M. Chinchu, H. Vennila2, G. S. Bibin, "Enhanced Deep Learning Approach for Cross-Domain Detection of Faults and Foreign Objects on Power Transmission Line," International Journal of Electrical and Electronics Engineering, vol. 13, no. 4, pp. 13-4, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I4P103

Abstract

The transmission power lines make up the bulk of the contemporary electrical infrastructure, and the constant operation of the power lines is critical to the well-being, stability of the economy, and the community. However, faults and foreign object penetration, such as contact with vegetation, conductor sagging, and flashover, are a significant threat, leading to power interruptions, equipment destruction, and safety hazards. Current fault detection methods are mostly based on single-modality analysis, manual inspection, or traditional deep learning models, which have low cross-domain awareness, low generalization, and low accuracy in complex environmental settings. To address these constraints, this work presents a deep learning method with improved cross-domain faults and foreign objects detection on power transmission lines. The proposed framework combines structured power system data and PMU fault images via a Unified Event-Centric Data Model (UECDM) and three significant novelties: accurate line segmentation with LineGuard-SegNet, optimal feature selection with RHO-MTS (ReliefF-guided Multi-Verse Optimizer with Tabu Search), and effective diagnosis with the TSTM-AttNet architecture with parallel multimodal branches and cross-modal attention fusion. The experimental results are superior with an accuracy of 98.8%, precision of 97.53, sensitivity of 98.62, specificity of 97.88, F1-score of 97.57, and outperform the current techniques on all evaluation measures.

Keywords

Power transmission line monitoring, LineGuard-SegNet, RHO–MTS feature selection, TSTM-AttNet, Predictive maintenance intelligence.

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