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

Wei Tang, Yue Zhang (), Xun Mao, Mingqi Shan, Kai Lv, Xun Sun and Zhenhuan Ding
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Wei Tang: State Grid Anhui Electric Power Research Institute, Hefei 100031, China
Yue Zhang: NARI Group Corporation Co., Ltd. (State Grid Electric Power Research Institute Co., Ltd.), Nanjing 211106, China
Xun Mao: State Grid Anhui Electric Power Research Institute, Hefei 100031, China
Mingqi Shan: NARI Group Corporation Co., Ltd. (State Grid Electric Power Research Institute Co., Ltd.), Nanjing 211106, China
Kai Lv: State Grid Anhui Electric Power Research Institute, Hefei 100031, China
Xun Sun: NARI Group Corporation Co., Ltd. (State Grid Electric Power Research Institute Co., Ltd.), Nanjing 211106, China
Zhenhuan Ding: School of Artificial Intelligence, Anhui University, Hefei 230001, China

Energies, 2025, vol. 18, issue 14, 1-15

Abstract: 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.

Keywords: power grid dispatch; UIE framework; trigger word; syntactic entities; entity recognition; event extraction; power grid power outage plan (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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