Data-Driven Algorithm for Disturbance Classification in Transmission Systems: Design, Implementation, and Experimental Evaluation

This paper investigates event detection and classification in power transmission systems based on real-world disturbance records from a part of the French 110 kV transmission grid. The study relies on measured voltage and current signals rather than simulated data. A novel signal-based algorithm is proposed for detecting abrupt changes in electrical signals and classifying power system events, enabling reliable identification of energizing events, no-load conditions, load changes, and single-phase, phase-to-phase, and three-phase faults. In addition to event classification, key transient characteristics such as fault inception time, event duration, peak short-circuit currents, and inception angle are extracted. By transforming raw disturbance records into structured and interpretable event…