2026· Communication in Physical Sciences· 0 citations
TL;DR
The proposed framework employs ANN as a nonlinear feature embedding and a Radial Basis Function SVM subsequently classifies using an Error-Correcting Output Codes (ECOC) strategy, validating the effectiveness of the proposed hybridisation strategy for intelligent transmission system protection.
Abstract
The detection of fault and its resolution are crucial in a power transmission line for ensuring unhampered and efficient power supply. These lines are often exposed to unpredictable environmental conditions and therefore encounter several challenges. Most failures in the power system are attributed to these, thereby necessitating the need for quick fault detection and resolution procedures. Research on hybridizing ANN and SVM is limited to fault detection and classification. Work on hybridizing ANN and SVM on IEE 39-bus for three simultaneous diagnostic tasks of fault type classification (LG, LL, LLG, LLL), faulted-line identification, and protection zoning defined as near-end versus far-end fault discrimination in a model is rare. This study bridges this gap by presenting a hybrid ANN-SVM model onfault type classification, fault line identification and protection zone identification in power transmission lines. The proposed framework employs ANN as a nonlinear feature embedding and a Radial Basis Function SVM subsequently classifies using an Error-Correcting Output Codes (ECOC) strategy. Evaluated on fault scenarios from the IEEE 39-bus New England test system simulated in MATLAB/Simulink R2025b, the hybrid model achieves fault type classification accuracy of 97.2%, protection zoning accuracy of 95.8%, and faulted-line identification accuracy of 97.7%, with ROC Area Under the Curve (AUC) values exceeding 0.90. These results consistently outperform standalone nd SVM baselines by 4% to 6% in fault type, 2.9% to 15.9% in protection zoning and 5% to 10% in fault line classification, validating the effectiveness of the proposed hybridisation strategy for intelligent transmission system protection.
A hybrid two-stage machine learning pipeline that decouples detection from classification is proposed, and the direction of the zero-sequence signature is found to be system-dependent, motivating a learned decision boundary in place of a fixed relay threshold.
Sahil Manikshete, A. Gujarathi, Thanh Long Vu et al.· 0 citations
The results indicate that phase-angle information provides supplementary and class dependent discriminative value, but does not consistently improve all fault classes, whereas conventional voltage and current measurements alone represent a simpler and more stable alternative, whereas phase-angle measurements may be incorporated when synchronized phasor information is already available.
Zeynep Bala Duranay, İsmail Anıl Avcı, Mohammed Bushra Mohammed et al.· Symmetry· 0 citations
The findings show that the suggested hybrid model works better than conventional techniques, with a fault classification accuracy of 98.66% as opposed to decision trees’ 97.42% and SE-CDAE’s 97.98% accuracy.
Qinghua Chen, Tao Xu, Cheng Zhou et al.· Distributed Generation &...· 0 citations
Power transmission lines are essential components of power systems, and their operating conditions directly affect the safety, stability, and reliability of power supply. In practical operation, transmission lines are susceptible to various fault conditions, such as short circuits, grounding faults, conductor breakage, and insulator abnormalities, due to lightning strikes, strong winds, pollution, equipment aging, and external disturbances. Conventional fault identification methods mainly depend on relay protection signals, manual inspection, and model-based analysis. Although these methods have been widely applied, they often suffer from limited adaptability, insufficient feature representation capability, and reduced identification accuracy under complex operating conditions. To improve the accuracy and intelligence level of transmission line fault diagnosis, this paper proposes a deep learning-based intelligent fault identification method for power transmission lines. The proposed method employs a deep neural network to learn fault characteristics directly from transmission line monitoring data, thereby reducing dependence on manual feature extraction. Through hierarchical feature representation and nonlinear mapping, the method can effectively distinguish different fault patterns and improve fault classification performance. In addition, preprocessing and optimization strategies are introduced to suppress noise interference and enhance model robustness under imbalanced and complex data conditions. Experimental results show that the proposed method achieves better identification accuracy, stability, and generalization performance than conventional methods. The proposed method can provide effective technical support for online fault monitoring, rapid fault diagnosis, and intelligent operation and maintenance of transmission lines.
Zhi-Wei Ni, Wen Chen, Pan Zhou et al.· European Conference on Elect...· 0 citations
The results confirm that the proposed framework provides a comprehensive and efficient solution for real-time fault analysis by combining classification, localization, temporal analysis, and stability-aware decision support within a single model.
Nazmun Nahar Karima, M. Hazari, Shameem Ahmad et al.· Energies· 0 citations
Power transmission lines, as the core of the power system, are prone to failures due to long-term exposure to outdoor environments. Traditional manual inspection and data analysis methods are unable to meet the requirements of intelligent operation and maintenance. This paper uses 1829 sets of transmission line fault data collected by drones, covering multiple features such as electrical and environmental aspects, and classifies them into five operational states and conducts variable correlation analysis. To address the problem of insufficient feature extraction and poor generalization of a single algorithm in fault identification, a TCN-Transformer algorithm that integrates local temporal feature extraction and global attention mechanism is proposed, and an end-to-end classification prediction framework is constructed. The data set is divided in a 7:3 ratio and core parameters are set. The results show that the model has an accuracy rate of 0.823 and an AUC of 0.972. It outperforms traditional machine learning and ensemble learning models such as decision trees, random forests, and XGBoost in multiple key indicators, effectively improving the accuracy and stability of transmission line fault identification in complex scenarios, and providing a feasible technical solution for intelligent fault discrimination in drone inspections.
