Fast Identification Of Topology Errors Based On Generalized State Estimation

The general step of power system state estimation is to deal with the breaker and analog separately.When there is an error in the topology analysis of the breaker,the final estimation result will not be available.The unified identification of breaker error and analog error can be realized by using the generalized state estimation model,but the model has the problems of too much calculation and poor real-time performance.Therefore,it is of great significance to study how to realize the fast identification of topology errors.In order to solve the above problems,a fast identification method of topology errors in power plants and stations is proposed based on generalized state estimation.This method can improve the speed of breaker topology identification and simplify the amount of calculation in the identification process.First of all,this paper makes a detailed analysis of traditional state estimation and generalized state estimation,points out the differences between the two methods and their respective problems,and analyzes the relationship between Lagrangian multipliers and quantitative measurement residuals,as well as topological error identification criteria.Secondly,based on the generalized state estimation model mentioned above,the constraint equation in the model is redescribed.According to the characteristics of the constraint equation,the global state variables are decoupled,and the decoupled state variables are divided into independent variables and dependent variables.Then,the measurement equation is expressed by independent state variables,and the structure of the measurement Jacobian matrix is analyzed,which simplifies the iterative equation and reduces the dimension of the matrix.Under the condition that the objective function is constant,the results of traditional state estimation can be fully utilized,and the global solution is divided into two steps.Finally,the analog bad data is identified by the measurement residual,and the state error of the breaker element is identified by the calculated Lagrangian multiplier.Then,this paper introduces the implementation process of a distributed parallel state estimation algorithm based on edge computing,which realizes the parallel state estimation based on power plant and station by using Zookeeper technology to store and communicate the shared information.Combined with the fast identification method of topology errors in power plants and stations proposed earlier,a distributed fast identification framework of topology errors in power plants and stations based on edge computing is established.In this scheme,the local data of the plant and its adjacent stations are obtained by Zookeeper technology to identify the state information errors of the breaker components of the plant and substation.after the parallel state estimation,the fast topology error identification based on the distributed parallel state estimation of the power station is realized,thus the distributed sinking of the computing power of the main station is realized,and the amount of calculation of topology identification is reduced.In the process of implementing and verifying the algorithm,the topology identification method is improved by analyzing the relationship between the average residual of the line and the estimated result when there is a topology error in the power station.According to the magnitude of the average residual value of the line,the suspicious plant and station with large residual pollution are determined,and then the criterion of edge topology recognition is realized,which further improves the speed of the algorithm.Finally,taking a 110 k V station in Shandong Province as an example,several different scenarios are designed,and the results of this method and the generalized state estimation method for topology error identification are compared and analyzed to verify the effectiveness of the proposed fast topology error identification method.The experimental results show that the iterative matrix dimension of the proposed method is lower,the memory is less,and the calculation speed is faster.