电网故障处置知识图谱构建研究与应用

Construction and Application of Power Grid Fault Handing Knowledge Graph

电网故障处置预案对电网事故应急工作的高效、有序进行有着重要的指导意义。利用知识图谱技术对故障处置信息进行知识抽取、表示和管理,并用于辅助调度人员进行故障处置,可有效提升电网应急处理能力与调度智能化水平。以电网故障处置预案文本为研究对象,提出了一种自顶向下和自底向上相结合的电网故障处置知识图谱构建方法,并解决了其中涉及的电力领域知识抽取问题。首先,自顶向下定义知识图谱的知识组织架构、概念类型、概念间关系,形成知识图谱的模式层;之后,针对电网故障处置预案文本的特性,综合使用多种深度学习模型进行知识抽取,自底向上构建知识图谱的数据层:为避免分词错误,使用基于字向量的TextCNN模型对预案文本进行分类;为解决候选词冲突问题,使用LR-CNN模型对电力领域的命名实体进行识别;在命名实体识别的基础上,使用BiGRU-Attention模型对实体间关系进行抽取。之后,通过实验验证了上述知识抽取方法的有效性。最后,对构建的电网故障处置知识图谱进行了可视化并对其在智能信息检索和辅助故障诊断中的应用进行了分析。

The fault handling pre-plan of power grid has great instructive significance for the quick emergency disposal when the failures or accidents occur.To assist dispatchers in fault handling and improve the capabilities of power grid's emergency handling and the level of dispatch intelligence,the technology of knowledge graph can be used to extract,represent,and manage fault handling pre-plan.By exploring the fault handling pre-plan of the power grid,this paper proposes a new method to construct the knowledge graph for the fault handling..The method combines both the top-down and bottom-up construction strategies and solves the involved problem of the knowledge extraction for the power domain.Firstly,the scheme layer of the knowledge graph in the top-down style is designed,which defines the knowledge framework,the concept types,and the relationships between the concepts of the knowledge graph.Then,according to the characteristics of the pre-plan text,multiple deep learning models are comprehensively used for knowledge extraction,and build the data layer of the knowledge graph in the bottom-up style.To avoid word segmentation errors,the TextCNN model is used based on character-level vectors to classify the text content of the plan.For the word conflict,the LR-CNN model is applied to identify domain named entities.On the basis of named entity recognition,the BiGRU-Attention model is adopted to extract the relationships between the entities.Finally,the effectiveness of the above-mentioned knowledge extraction method is verified through experiments.The constructed power grid fault handling knowledge graph is visualized and its application in intelligent information retrieval and auxiliary fault diagnosis is analyzed.