Enhancing reliability in electrical grids: A hybrid machine learning approach for electrical faults classification

Transmission lines are vital components of electrical grids, ensuring the efficient transfer of electricity from power plants to consumers over extensive geographical areas. These lines are constructed with careful consideration of factors such as conductor materials, insulation levels, current ratings, and voltage ratings to maintain reliable and safe electricity delivery. However, various types of faults can occur in transmission lines, posing significant challenges, often leading to outages, equipment damage, and reduced system reliability. Accurate and fast fault classification is therefore a pressing requirement in modern smart grids, where proactive maintenance and resilience are critical. This research addresses the critical need for an efficient electric fault classification model. A comprehensive investigation is conducted, employing a variety of machine learning (ML) algorithms, including Decision Tree (DT), Random Forests (RF), Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and, AdaBoost, for fault classification. Additionally, fundamental ensemble techniques such as Hard-Voting, Soft-Voting, Stacking, and Blending are incorporated with five hybrid ML models (each constructed by combining various ML algorithms) to enhance fault classification performance and the reliability of transmission lines. Also, this research proposes a hybrid ML model, specifically (RF + DT + Stacking) , to classify transmission line data. The main contribution of this work is an application-oriented evaluation of classical and ensemble machine learning models for electrical fault classification, with an emphasis on benchmarking performance, model interpretability, and computational efficiency. This study demonstrates that a carefully configured hybrid ensemble (RF + DT + Stacking) can provide a practical and lightweight alternative to deep learning-based methods in grid fault monitoring scenarios. The dataset used encompasses various attributes affecting line performance, making accurate classification critical for proactive issue detection, optimized maintenance scheduling, and uninterrupted energy supply. Our hybrid model achieves high-performance metrics, including an accuracy of 93.64%, precision of 93.65%, recall of 93.64%, and F1 score of 93.64%, underscoring its effectiveness in enhancing decision-making processes and operational efficiency within electrical transmission networks.