摘要
知识图谱将配电网现有大量半结构化/非结构化文本数据关联,提高配电网故障处置效率,但配电网多源异构文本数据难以用于深度学习模型训练且电力领域文本数据的标注成本较高。该文采用预训练方法构建深度学习模型对故障处置数据进行命名实体识别,利用知识图谱技术对数据进行知识存储和应用,辅助调控人员进行故障处置决策。首先,以配网设备台账数据、故障处置数据、调度规程数据及配网缺陷库数据为对象,提出配电网故障处置知识图谱的构建框架和方法;然后,针对配电网可用于深度学习训练的数据量不足的问题,采用预训练方法构建了实体识别模型,实现了配电网领域非结构化知识的抽取;接着,设计实验证明了该文所构建模型的有效性,模型的F1值达到86.3%,准确率达到95.16%;最后,利用Neo4j图数据库对知识图谱进行高度可视化管理,并给出配电网故障处置知识图谱的应用流程,有效提高配电网调控人员故障处置决策效率和处置效果。
Knowledge graph associates a large number of semi-structured/unstructured text data in the distribution network to improve the efficiency of the distribution network fault handling.However,it is difficult to use the multi-source heterogeneous text data in the distribution network for the deep learning model training,and there is a high labeling cost of the text data in the power field.In this paper,the pre-training method is used to build a deep learning model to identify the named entity of the fault handling data.The knowledge graph technology is used to store and apply the data so as to assist the regulators in making fault handling decisions.Firstly,taking the distribution network equipment account data,the fault handling data,the dispatching regulation data and the distribution network defect data as the objects,the framework and method of building the knowledge graph for the distribution network fault handling are proposed;Then,aiming at the problem of insufficient data available for the deep learning model training in the distribution network,the entity recognition model is constructed by using the pre-training method to extract the domain unstructured knowledge of the distribution network;Next,the design experiment proves the effectiveness of the model constructed in this paper with the F1_score of the model as 86.3%and the accuracy as 95.16%;Finally,the Neo4 j graph database is adopted for highly visual management of the knowledge graph.The application process of the distribution network fault handling knowledge graph is given,which can effectively improve the decision-making efficiency and handling effect of the distribution network regulators.
作者
叶欣智
尚磊
董旭柱
刘承锡
田野
方华亮
YE Xinzhi;SHANG Lei;DONG Xuzhu;LIU Chengxi;TIAN Ye;FANG Hualiang(School of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,Hubei Province,China;Electric Power Research Institute,State Grid Liaoning Electric Power Co.,Ltd.,Shenyang 110006,Liaoning Province,China)
出处
《电网技术》
EI
CSCD
北大核心
2022年第10期3739-3748,共10页
Power System Technology
基金
国家电网有限公司总部管理科技项目(5400-202128154A-0-0-00,基于多源物联信息融合的供电可靠性业务提升技术研究与应用)。
关键词
配电网
深度学习
故障处置
知识图谱
知识抽取
预训练
distribution network
deep learning
fault handling
knowledge graph
knowledge extraction
pre-training
