A hybrid classification model for high-precision fault mode detection in three-phase electrical networks
Year: 2026
Type: article
Abstract: In modern electric power systems, accurately identifying short-circuit conditions is essential for reliable network monitoring and protection. This study proposes a hybrid machine learning framework for multi-class fault mode classification in three-phase electric networks. The experimental dataset included 15 operating modes, including normal mode, single-phase mode, phase-to-phase mode, three-phase mode, and ground fault mode. Input variables included phase current measurements and positive, negative, and zero-sequence current components. The preprocessing pipeline included logarithmic transformation, standardization, and correlation-based feature selection. The processed data was split into training and independent test subsets in an 80/20 ratio. Next, k-means clustering with 15 clusters was used to generate additional structural features, while random forest and extreme gradient boosting (XGBoost) algorithms created probabilistic class predictions, which were integrated by a logistic regression metaclassifier. According to the adopted testing protocol, the proposed hybrid model achieved an accuracy of 96.98%, a macro-precision of 0.973, a macro-recall of 0.969, and a macro-F1-score of 0.968, outperforming the baseline random forest model, which achieved an accuracy of 93.91% and a macro-F1-score of 0.937. These results demonstrate the potential of the proposed multi-level framework for accurate multi-class fault diagnosis in three-phase electrical networks.
Institutions: L. N. Gumilyov Eurasian National University, Khalel Dosmukhamedov Atyrau University, Zhangir Khan West Kazakhstan Agrarian Technical University, Sarsen Amanzholov East Kazakhstan University, Kazakh National Medical University, Astana Medical University, M. Kh Dulati Taraz State University, Dulaty University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7214955823