Research On Critical Transient Stability Boundary Feature Extraction Method Based On Data Mining

The boundary problem of critical transient stability is one of the core issues in evaluating the transient stability of power systems.At present,in the transient stability assessment,it is often impossible to give an accurate judgment on the critical state of the power grid,so that the safety assessment has a fuzzy region.It is of great significance to characterize the boundary characteristics of critical transient stability for the refined evaluation of transient stability of power systems.The traditional solutions to the transient stability boundary problem are mainly numerical analysis,transient energy function and trajectory analysis.The former mainly determines the critical ablation time by means of iterative solution,and then determines the critical transient steady state.Although the method is accurate,the calculation is large.The latter two methods mainly start from the energy function,and then obtain the critical energy value or the critical fixed value,but the model adaptability is poor and the process of obtaining is complicated.In recent years,with the gradual maturity of artificial intelligence and big data technology,pattern recognition methods based on data analysis have gradually provided new ideas for solving some traditional problems in power systems.This kind of method mainly analyzes the correlation between each state information by mining and establishes a data-driven perceptual analysis model,and then mines the other side of the grid from the perspective of data.To this end,based on the in-depth summary of previous work,this paper draws on the idea of artificial intelligence image recognition,and carries out a series of work based on data mining for critical transient stability boundary feature extraction.The main research work of this paper includes the following contents:(1)Through the description of data mining technology and its application in power system,it provides a new way to solve the problem of extracting the critical transient stability boundary feature of traditional power grid.Re-reviewing the boundary problem of power system from the perspective of data,and supporting the research on the extraction of critical transient stability boundary features based on data mining.(2)Analyze and compare the power angle trajectory of the generator after the fault,and visually describe the boundary phenomenon of critical transient stability.At the same time,in order to realize the rapid and automatic generation of boundary samples,a batch generation method of boundary samples,two-end approximation method,and automatic simulation program development based on PSD-BPA is proposed to provide data support for transient boundary analysis based on data mining.(3)Based on the idea of artificial intelligence image recognition,based on the correlation analysis,the input features that best characterize the operating state of the power system and are closely related to each other are selected.A "source-network" feature matching method based on SIFT algorithm is proposed to complete the matching of "source-network" feature vectors.At the same time,in order to quantify the matching result,the matching degree index is constructed,and the boundary characteristics of critical transient stability are characterized by the matching degree index.Finally,the algorithm is verified by an example.(4)In order to realize the engineering application of critical boundary characteristics of power grid,this paper proposes a new generation of smart grid control system framework based on data drive,and implements algorithm verification and visualization development through laboratory cluster environment.In summary,this paper focuses on the boundary problem of critical transient stability,and carries out a series of research work from the perspective of data analysis,which is of great significance for realizing the refined evaluation of grid transient stability,and has a data-driven grid analysis mode.Actively explore value.