Intelligent Fault Diagnosis in 330 kV Power Networks Using SVM and ANN Techniques: Case of the Onitsha-New Haven Route

Abstract

This paper investigated the application of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) for transient fault detection and classification in power transmission systems, focusing on Nigeria’s Onitsha-New Haven 330 kV network. A Multilayer Perceptron (MLP) ANN, optimised with 10 hidden layers and the Levenberg-Marquardt algorithm, achieved near-perfect regression (correlation coefficient R > 0.999 ) and classification accuracy (≈99%) using 9,180 operational samples, validated against IEEE-compliant transient stability indices (Si) and dynamic voltage margins (DVm). Comparatively, SVMs demonstrated ~95–98% accuracy with sub-5  inference times, using pre-engineered features (such as harmonic distortions and wavelet coefficients) for real-time efficiency in resource-constrained environments. The study proposed a hybrid ANN-SVM framework, combining SVM’s rapid fault detection with ANN’s precision for post-event diagnostics, addressing the speed-accuracy trade-off in dynamic grids. Engineering implications highlight enhanced grid resilience, cost-effective deployment strategies, and support for renewable integration. At the same time, contributions include empirical validation of AI models in sparse-data contexts and methodological benchmarks aligning machine learning with power quality standards. Recommendations advocated phased AI adoption, workforce training, and regulatory standardisation, with future research directions spanning hardware-in-the-loop validation, cybersecurity, and adaptive learning for renewable-rich grids. This work bridges theoretical AI advancements with practical power system needs, offering scalable solutions for global energy transitions.