知识驱动的大电网仿真分析知识建模方法及其在潮流智能调整问题中的应用

Knowledge Modeling Method for Simulation Analysis of Large-scale Power System and Their Application in Automation Adjustment of Power Flow Driven by Knowledge

大电网数字仿真分析需要耗费大量的人力和时间成本来进行综合仿真分析,其分析和调整过程严重依赖人工经验知识,且由于经验知识表述的多样性和应用场景的特殊性,缺乏一套统一的知识表示方法。为此,提出一种电网人工智能仿真分析的知识建模方法。根据电网数字仿真分析调整知识经验的特点,将定性与定量、关联与事理知识相结合,分别针对调整流程、调整操作和程序调用设计基于主谓宾语(subject-predicate-object,SPO)的知识表示方法;搭建模块化的知识驱动模型将电网仿真分析过程智能化;设计一套知识抽取与清洗方法来获取所需的三元组形式知识,利用获得的知识三元组构建知识图谱,开发可视化的知识库管理系统。在改进的CEPRI36节点系统和东北电网上进行潮流计算收敛调整仿真实验,验证方法的可行性和有效性。

The digital simulation analysis for power grid requires a lot of manpower and time cost to comprehensively analyze simulation.The process of analysis and adjustment heavily relies on manual experience knowledge which lacks a unified form of knowledge representation method,because of the diversity of expression and particularity of application scenario.Thus,a knowledge modeling method for artificial intelligence simulation analysis of power grid is proposed.According to the characteristics of adjustment knowledge experience for digital simulation analysis,a knowledge representation method based on subject-predicate-object(SPO)is designed for adjustment process,adjustment operations and program calls,which combines qualitative and quantitative,correlation and affair knowledge.A modular knowledge-driven model is built to automate the process of power grid simulation analysis.A set of knowledge extraction and cleaning method is designed to obtain the required knowledge in the form of triples,and the knowledge graph is constructed by using the obtained knowledge triples which is developed to a visual knowledge base management system.The experiments of convergence adjustment for power flow calculation are carried out on the improved CEPRI36 node system and northeast power grid,which verify the feasibility and effectiveness of the method.