Construction and Application of Knowledge Graph for Power Grid New Equipment Start-Up

Wei Tang, Yue Zhang (), Xun Mao, Hetong Jia, Kai Lv, Lianfei Shan, Yongtian Qiao and Tao Jiang
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Wei Tang: State Grid Anhui Electric Power Research Institute, Hefei 230601, 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 230601, China
Hetong Jia: 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 230601, China
Lianfei Shan: NARI Group Corporation Co., Ltd., (State Grid Electric Power Research Institute Co., Ltd.), Nanjing 211106, China
Yongtian Qiao: NARI Group Corporation Co., Ltd., (State Grid Electric Power Research Institute Co., Ltd.), Nanjing 211106, China
Tao Jiang: NARI Group Corporation Co., Ltd., (State Grid Electric Power Research Institute Co., Ltd.), Nanjing 211106, China

Energies, 2025, vol. 18, issue 20, 1-14

Abstract: To address the lack of effective risk-identification methods during the commissioning of new power grid equipment, we propose a knowledge graph construction approach for both scheme generation and risk identification. First, a gated attention mechanism fuses textual semantics with knowledge embeddings to enhance feature representation. Then, by introducing a global memory matrix with a decay-factor update mechanism, long-range dependencies across paragraphs are captured, yielding a domain-knowledge-augmentation universal information-extraction framework (DKA-UIE). Using the DKA-UIE, we learn high-dimensional mappings of commissioning-scheme entities and their labels, linking them according to equipment topology and risk-identification logic to build a commissioning knowledge graph for new equipment. Finally, we present an application that utilizes this knowledge graph for the automated generation of commissioning plans and risk identification. Experimental results show that our model achieves an average precision of 99.19%, recall of 99.47%, and an F 1 -score of 99.33%, outperforming existing methods. The resulting knowledge graph effectively supports both commissioning-plan generation and risk identification for new grid equipment.

Keywords: new equipment start-up; memory matrix; knowledge augmentation; UIE framework; knowledge graph; risk identification (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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