Research On The Model And Algorithm Of Large-scale Power Grid Load Situation Awareness Based On RMT

Facing the random fluctuation and complex coupling of power grid,it has become an important trend of power grid development to introduce the concept of situational awareness into the operation control of large-scale power grid.On the other hand,with the continuous promotion of Wide Area Measurement System(WAMS)and the rapid evolution of information and communication technologies such as 5G,the explosive growth of data sets challenges for data processing and knowledge extraction,and provides better conditions for situational awareness technology.Random matrix theory(RMT),as a data-driven analysis method,takes the big-dimensional statistical principle as the foundation and the eigencharacteristics(eigenvalues and eigenvectors)as the evaluation indexes,which can reflect the current running state of the system from a high-dimensional perspective.Therefore,it has important theoretical significance and considerable application potential to carry out research on RMT-based situational awareness model and algorithm of large-scale power system.Firstly,this paper introduces the research background of the subject,summarizes the research status at home and abroad,then presents the power data model and pretreatment method that are suitable for RMT,and explains the basic law in RMT.Finally,it analyzes the change of system statistical characteristics when the load of the power grid is abnormal.Secondly,this paper analyzes and studies the adaptability of six RMT-based detection indexes in power system,and explains the meanings of these indexes.By comparing the recognition results of each index on abnormal load in different SNR environments,it is shown that the Maximum Eigenvalue of Sample Covariance Matrix(MESCM)index has higher anti-noise performance,less computation time,and is more suitable for the recognition of abnormal load in power system.Thirdly,from two aspects of Matrix characteristics,a method for locating multiple disturbances of power grid is proposed based on Minimum Eigenvector of Sample Covariance Matrix(MERSCM)and MESCM.The method with the help of a model based on Spiked MESCM dynamic threshold and MERSCM based multiple disturbance "phase change" to realize the power grid.The simulation results of an IEEE 54-machine 118-node system show that the proposed method is effective and efficient.Finally,based on MESCM and MERSCM in RMT,a method for fast identification and location of abnormal load in large-scale power grid is proposed by using improved Rayleigh entropy and partition parallel computing technology.The results of an IEEE 54-118 node system and a polish 420-2736 bus system are compared with those of the traditional msr-based method to verify the effectiveness and efficiency of the proposed method.