Research On Identification Method Of Distribution Network Topology And Line Parameters Based On Measurement Data

Precise distribution topology and line parameters are the basis of power system analysis such as state estimation,power flow calculation and setting calculation.They are also the prerequisite for power system planning,operation and control.Especially with the access of distributed power sources,in order to cope with the impact of uncertainty on the safety and reliability of the distribution network,it is necessary to rely on accurate topology and line parameters to dynamically reconfigure and intelligently control the topology.Compared with the transmission network,the distribution network has fewer real-time monitoring equipment,and most of the topology and line parameter information can only be obtained from the grid planning documents and nameplates.Especially the line parameters,due to the long-term operation,weather conditions,maintenance and so on,the actual parameters and the theoretical parameters are deviated.Therefore,based on the measurement data provided by the smart meter,this paper carries out the research work on the identification of the distribution network topology and line parameters.The main contents are as follows:First,for the problem of distribution network topology identification,a distribution network topology identification method based on mutual information and support vector machine（SVM）is proposed.Taking the p,q,and v measurement data of the smart meter as the original feature set,the mutual information theory is used to calculate the topological identification contribution of each feature to obtain the optimal feature subset.On this basis,take the optimal feature subset as input and topology label as output,and the optimal parameters of SVM are found with the test-and-error method.Thus,the SVM distribution network topology identification model is established,which can achieve the mapping between the measurement data and the topology structure.In addition,in order to determine whether there is an unknown topology,the Pearson correlation coefficient between the voltage data is calculated to update the topology library in the SVM multi-classification model.Then,on the basis of the SVM topology identification model,in order to realize the joint identification of the dynamic topology and line parameters of the distribution network,the paper uses historical metering data under topological structures to establish the multi-classification model based on SVM and the joint identification initial model based on linear regression.The SVM multi-classification model is used to realize the mapping between the online metering data and the topology,and the initial values of the topology and parameters are obtained.In this case,the accurate identification results are obtained by combining the modified model of topology and line parameter identification.In addition,to improve the numerical stability,orthogonal matrix and right triangular matrix decomposition is used to solve the linear equations in identification process.The effectiveness of the method is verified by IEEE 33-bus and PG＆E69-bus distribution network.Finally,in view of the large number of T-bus in the distribution network,a joint identification method of distribution network topology and line parameters based on improved linear regression is proposed.On the basis of the voltage drop equation,a multiple linear regression model is established by using the measurement data of multiple times.In addition,by analyzing the goodness of fit of the regression model,the judgment criterion for the series and parallel relationship of the two nodes is established,so as to restore the equivalent parameters of T node.Finally,using the p,q,v measurement data of multiple time sections,the network topology and line parameters are obtained from the leaf nodes from the bottom to the top.The method is verified by a power distribution system simulation example.