Research On Power Grid Security Situation Prediction And Modeling Based On Comprehensive Vulnerability Index And RBF Neural Network

The safe and stable operation of the power system network is the guarantee for the orderly life of the people and the stable growth of the economy.With the continuous increase of the scale of the power system in various countries around the world,the system contains more and more components,and the structure of the power system network is increasing.The more complicated.In order to improve the robustness of the system,the design of the control system becomes more complex,which enhances the coupling between various parts of the system,but also increases the possibility of cascading failures in the power system.Therefore,a more in-depth study of the impact of fragility on the security of large-scale power grids is the basic work to prevent and reduce large-scale cascading blackouts.However,traditional analysis methods based on the electrical characteristics of power systems fail to properly consider the impact of the overall structure.The development and application of complex network theory has made up for the shortcomings of the power system’s vulnerability.Based on this research background,the main research contents of this article are as follows:Combined with the characteristics of the power system,the equivalent impedance is used to replace the shortest path in the complex network theory to improve the distance between nodes in the traditional complex network,and based on this equivalent electrical distance,the correlation degree of the grid node system is defined,which is used as the analysis of the vulnerability of the grid node.index.Through the simulation calculation of the IEEE39 node system,it was verified that the cumulative distribution function of the node system correlation degree satisfies the characteristics of the scale-free network,and the comparison and analysis with the voltage margin and the node voltage offset index verified the node system correlation degree analysis of the grid node The effectiveness of the vulnerability.Based on the power transmission distribution factor(PTDF),considering the impact of generation capacity,load level and line capacity on line vulnerability,an improved line electrical betweenness index was established,and the power factor,line load rate offset and node voltage offset indexes were integrated Constructed the comprehensive vulnerability assessment index of the route.Through the analysis of the example of the IEEE39-bus system,it is found that the comprehensive index is correlated with the change trend of the voltage margin and the line power flow distribution index,which can effectively analyze the vulnerability of the line.Comprehensively considering the five indicators of voltage margin,node voltage offset,line flow distribution,line load rate and line overload degree,dividing the grid security situation into node safety situation and line safety situation,and assign different weights to calculate the grid safety situation value to assess the current security status of the power grid.And the Radial Basis Function(RBF)neural network is applied to the prediction of power grid security situation.After comparing with Elman and Back Propagation(BP)neural network,the RBF prediction effect is best in three models,it can better predict the power grid security situation.In summary,in view of the security research issues based on power grid vulnerability analysis,a comprehensive vulnerability index of node system correlation and improved line electrical betweenness is proposed to analyze the vulnerability of power grid nodes or lines,and the power grid security situation value Comparing with the grid vulnerability index,analyzing the correlation between the two,it is concluded that the grid security situation value can reflect the current security status of the grid to a certain extent.At the same time,the application of RBF neural network to predict the grid security situation has certain engineering practical significance.