Similarity matching method of power distribution system operating data based on neural information retrieval

作     者：Kai Xiao Daoxing Li Pengtian Guo Xiaohui Wang Yong Chen Kai Xiao;Daoxing Li;Pengtian Guo;Xiaohui Wang;Yong Chen

作者机构：China Electric Power Research Institute Co.Ltd.Beijing 100192P.R.China 

出 版 物：《Global Energy Interconnection》 (全球能源互联网（英文版）)

年 卷 期：2023年第6卷第1期

页      面：15-25页

核心收录：

学科分类：1205[管理学-图书情报与档案管理] 0808[工学-电气工程] 080802[工学-电力系统及其自动化] 08[工学] 0701[理学-数学] 

基　　金：supported by the National Key R&D Program of China(2020YFB0905900). 

主　　题：Neural information retrieval Power distribution Graph data Operating section Similarity matching 

摘      要：Operation control of power systems has become challenging with an increase in the scale and complexity of power distribution systems and extensive access to renewable energy.Therefore,improvement of the ability of data-driven operation management,intelligent analysis,and mining is urgently required.To investigate and explore similar regularities of the historical operating section of the power distribution system and assist the power grid in obtaining high-value historical operation,maintenance experience,and knowledge by rule and line,a neural information retrieval model with an attention mechanism is proposed based on graph data computing technology.Based on the processing flow of the operating data of the power distribution system,a technical framework of neural information retrieval is established.Combined with the natural graph characteristics of the power distribution system,a unified graph data structure and a data fusion method of data access,data complement,and multi-source data are constructed.Further,a graph node feature-embedding representation learning algorithm and a neural information retrieval algorithm model are constructed.The neural information retrieval algorithm model is trained and tested using the generated graph node feature representation vector set.The model is verified on the operating section of the power distribution system of a provincial grid area.The results show that the proposed method demonstrates high accuracy in the similarity matching of historical operation characteristics and effectively supports intelligent fault diagnosis and elimination in power distribution systems.