Line Parameter Identification in Distribution Networks Based on Partition and Sliding Time Window Strategies
Targeting the problems of low accuracy and difficulty in line parameter identification in distribution networks,the paper proposes a method for identifying the line parameters of the distribution network based on the partition and sliding time window strategies.Firstly,the initial identifying parameters of the line are obtained by using the linear decoupling power flow model and least squares regression.Then the network is divided into several regions,and the line parameters in each region are accurately identified using the Gauss-Newton method within a sliding time window.Finally,outlier detection is conducted on the results obtained from all time windows,and the final identifying value is synthesized from the identifying results of each time window.This method improves the precision of the line parameter identification and can avoid the deviation of identifying results from the real value caused by the selection of bad data during the identifying process.
distribution networkline parameter identificationnetwork partitionsliding time windowdata-driven
万方数据
维普
