Type recognition and parameter identification of power quality disturbances using improved 1D-YOLO and multi-task learning

• 1D-YOLO framework unifies type recognition and time localization. • Element-Level analysis model of power quality disturbances. • Decoupled-heads used for the inclusion of characteristic parameter identification. • Multi-task learning contribute to the efficiency of the analysis models for PQDs. Accurate recognition and parameter identification of Power Quality Disturbances (PQDs) are crucial for effective disturbance management. This research presents a model built on an improved One-Dimensional You Only Look Once (1D-YOLO) framework, incorporating multi-task learning to unify three tasks: type recognition, time localization, and characteristic parameter identification. The proposed model utilizes a Path Aggregation Network (PANet) for advanced multi-scale feature fusion, while decoupled heads enable concurrent analysis of 10 PQD elements. The case study results indicate that the model achieves over 99.5% mean average precision and classification accuracy for both element-level and signal-level recognition tasks. Additionally, it precisely identifies starting-ending times, as well as key parameters, with multi-task learning further enhancing the performance of individual tasks.