Enhanced Named Entity Recognition and Event Extraction for Power Grid Outage Scheduling Using a Universal Information Extraction Framework

To enhance online dispatch decision support capabilities for power grid outage planning, this study proposes a Universal Information Extraction (UIE)-based method for enhanced named entity recognition and event extraction from outage documents. First, a Structured Extraction Language (SEL) framework is developed that unifies the semantic modeling of outage information to generate standardized representations for dual-task parsing of events and entities. Subsequently, a trigger-centric event extraction model is developed through feature learning of outage plan triggers and syntactic pattern entities. Finally, the event extraction model is employed to identify operational objects and action triggers, while the entity recognition model detects seven critical equipment entities within these operational objects. Validated on real-world outage plans from a provincial-level power dispatch center, the methodology demonstrates reliable detection capabilities for both named entity recognition and event extraction. Relative to conventional techniques, the F 1 score increases by 1.08% for event extraction and 2.48% for named entity recognition.