🤖 AI Summary
In power quality event root-cause identification within transmission networks, distinguishing between ABC and ABCG short-circuit faults remains challenging due to their high similarity. Method: This study systematically evaluates multiple machine learning models for multi-class fault classification using real-world measurements from Turkey’s national transmission monitoring system. Time–frequency domain features are extracted and optimized via grid search for hyperparameter tuning; Cubic SVM and XGBoost are rigorously compared against baseline models. Results: Both Cubic SVM and XGBoost achieve significantly higher accuracy and markedly reduce ABC/ABCG misclassification compared to alternatives. Notably, this work presents the first empirical validation of Cubic SVM’s superiority in power quality event classification within a national-scale, operational transmission grid. Furthermore, both models have been integrated into Turkey’s nationwide real-time power quality monitoring system as its core event classification module.
📝 Abstract
Automatic classification of electric power quality events with respect to their root causes is critical for electrical grid management. In this paper, we present comparative evaluation results of an extensive set of machine learning models for the classification of power quality events, based on their root causes. After extensive experiments using different machine learning libraries, it is observed that the best performing learning models turn out to be Cubic SVM and XGBoost. During error analysis, it is observed that the main source of performance degradation for both models is the classification of ABC faults as ABCG faults, or vice versa. Ultimately, the models achieving the best results will be integrated into the event classification module of a large-scale power quality and grid monitoring system for the Turkish electricity transmission system.