Research On Power Grid Operation Situation Awareness Based On Graph Digital Twi

The power grid is a highly intricate industrial system in modern society.As the power grid becomes more complex,the volume of generated data increases,and the importance of its stable operation also grows.Situation awareness is crucial to ensuring the stable operation of the power grid.It enables the analysis and prediction of the power grid by harnessing the relationships between its components.Digital twin technology can effectively map the physical world into the digital space in real-time,enabling timely identification of issues in the power grid.The combination of digital twin and situation awareness can enhance the ability to ensure the stable operation of the power grid.This paper proposes a power grid situation awareness model based on the digital twin approach.This model can achieve high-precision fault location and stability prediction in the power grid.It can also enable effective power grid situation awareness even when data integrity and authenticity issues arise.The main research contents of this paper are as follows:Firstly,this paper proposes a data graph-structured method to represent physical entities in the digital world,taking into account the characteristics of power grid data.The proposed approach maps data generated by the physical world to a graph,allowing for the construction of a graph digital twin model that contains complete information about the physical world in the digital domain.Building upon this graph digital twin model,the paper proposes a power grid situation awareness model that is primarily designed for fault location and stability prediction in situation awareness.By utilizing this model,it becomes possible to obtain a comprehensive understanding of the current state of the power grid,enabling more effective identification of faults and predictions of system stability.Secondly,considering the potential integrity issues of power grid data,such as missing component data and time series data,this paper proposes a model based on graph digital twin for power grid situational awareness that is compatible with missing component and time series data.That is,even in the case of data loss,high-precision power grid fault location and stability prediction can still be achieved.Thirdly,power grid operation is vulnerable to data authenticity issues caused by false data injection attacks.Such attacks can hamper the accurate analysis of the current state of the system and undermine the ability to forecast its future state,posing significant challenges for power grid operation.To tackle this challenge,this study proposes a detection method based on long short-term memory(LSTM)networks to enable highprecision identification of false data injection attacks.The proposed approach leverages the detected genuine data for power grid situational awareness,ensuring robust model performance.Fourthly,a novel approach based on graph convolutional network has been developed for analyzing and processing graph structured data.This approach involves two classifiers,with the first classifier being node-focus for fault localization,and the second classifier being graph-focus for predicting stability.The experimental results demonstrate that this proposed model can effectively handle situations involving incomplete data and false data injection attacks in power grids.It can accurately locate multiple fault components with high precision and also predict the stability of the system with accuracy.