Jul 2026· International Conference Computing Methodologies and Communication· pp. 1-6· 0 citations· 16 references
Abstract
A paradigm shift in energy grid operations has arisen from swift improvements in measurement and computer technology, allowing contemporary systems to integrate self-healing functionalities. An essential prerequisite for these systems is the fast identification and localisation of transmission line faults to guarantee grid reliability and expedite service restoration. This research introduces an innovative SVKN method for precise detection and localisation of transmission line problems. Voltage and current signals undergo initial preprocessing before being decomposed with the Wild Horse Optimisation (WHO) method to isolate high-frequency features and low-frequency approximations. The amalgamation of Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) facilitates efficient classification and localisation. The findings indicate that the suggested SVKN method surpasses current models such as SVKN, SVM, KNN, Two Stage KNN and Two Stage SVM, in both fault type classification and the identification of the impacted transmission line. The SVKN model attained an accuracy of 94.13% in predicting fault positions, hence validating its precision. The suggested SVKN framework markedly enhances Transmission Line Fault Identification and Localisation, providing a dependable and effective solution for real-time fault management in advanced smart grids.
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.
Kudu Abubakar Mohammed, M. Balogun, Adesina M. Lambe et al.· Communication in Physical Sc...· 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
To address the issues of insufficient feature extraction and low localization accuracy in distribution network fault diagnosis, this study proposes a fault classification and localization method based on APC-SVM and PC-AZOA. The model performs a simultaneous decomposition of three-phase signals using multivariate variational modal decomposition and employs the energy entropy of each model component as the feature vector; During the classification stage, the method integrates electrical and physical constraints, introducing three-phase energy imbalance and variance into the support vector machine ’ s parameter optimization process for the first time to dynamically adjust the penalty factor and kernel parameters; finally, a traveling wave propagation time error model is constructed, and an adaptive zebra optimization algorithm constrained by physical information is proposed. By innovatively embedding prior physical knowledge into the search space constraints, the method effectively suppresses invalid searches and improves convergence efficiency. Experimental results show that the model achieves a classification accuracy of up to 98.4% with a positioning error below 1%, demonstrating both high precision and high efficiency.
Dahua Li, Xinrui Yang, Yu Song et al.· 2026 IEEE International Conf...· 0 citations
A two-stage WT-PSO-RBFNN framework in which adaptive db4 level-3 wavelet thresholding performs fault detection and a PSO-optimized radial basis function neural network performs phase/ground classification using normalized wavelet-domain features is proposed.
Kabiru Dahiru Ibrahim, C. Ukwuoma, Ahmad Abubakar et al.· Cluster Computing· 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
This study develops a low-complexity data-driven method for real-time fault classification, and localization on a 20 kV radial feeder, while accounting for the computational and memory constraints of embedded hardware. The proposed approach is motivated by the changes introduced by distributed generation (DG), which alters fault-current behavior and reduces the reliability of conventional impedance-based protection. These limitations are mainly caused by reverse power flows and the additional infeed contribution from DG units during fault conditions. The proposed hybrid architecture processes time- and frequency-domain features, including symmetrical components and wavelet energy, through a dual-stage inference chain. The first stage uses a Random Forest classifier for fault-type identification, while the second stage uses a Multi-Layer Perceptron (MLP) distance estimator for fault localization. Evaluation on 23,778 MATLAB/Simulink fault scenarios, was performed using a grouped scenario-level holdout split to reduce overlap between training and test cases. The proposed approach reduces the localization error compared with the conventional impedance method, which produced errors exceeding 14 km under DG operation. The Random Forest classifier achieved 99.44% fault-type accuracy, and the MLP estimator reached a Mean Absolute Error (MAE) of 93.8 m, with localization accuracy of 99.51% within 1 km. Analytical resource profiling estimates less than 300 KB Flash and under 50 µs execution time, assuming a Cortex-M4F/M7-class target, pending hardware-in-the-loop validation.
M. Lemkharbech, S. Sarih, Z. Boulghasoul et al.· International Conference on...· 0 citations
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