Design and Implementation of the Data Knowledge Hybrid Driven Automatic Calculation and Analysis Framework for Power Grid Simulation

Power grid simulation is a supporting technology for power grid planning and operation, widely used in the calculation and analysis of power grid operation modes. However, the current power grid simulation analysis work mainly relies on manual labor, which is difficult to meet the increasingly complex requirements of large-scale power grid simulation analysis. It is urgent to carry out research on automatic calculation and analysis technology and framework for power grid simulation, and to achieve effective support of artificial intelligence technology for simulation analysis work. This article adopts a data knowledge hybrid driven artificial intelligence method. Firstly, the knowledge and rules required for simulation are obtained through expert organization and data mining, and a knowledge graph for automatic simulation calculation and analysis is constructed. Secondly, a state transition diagram is used to model the automatic calculation process of power grid simulation, and a power grid simulation state transition diagram corresponding to the power grid simulation task is established. By defining the action that causes the state transition on the state transition diagram, as well as the knowledge rules such as input data and constraints required for the action to be automatically executed by the machine, the automatic transition and execution of each state in the state diagram are achieved. Finally, a data knowledge hybrid driven power grid simulation automatic calculation and analysis framework is implemented based on the Neo4j graph database and Python language, and applied to the automatic calculation and analysis of transmission capacity of power grid section with limited transient stability.