Research On State Estimation Of Distribution Network

The distribution network automation construction is becoming more and more complete.On the basis of the supervisory control and data acquisition(SCADA),it has successively access to advanced metering infrastructure(AMI)and micro-synchronous synchronization(μPMU).These systems bring a lot of data to the state estimation,and this paper studies the state estimation of the distribution network based on these data,which helps the system dispatcher obtain the operation state of the distribution network more accurately.Topic selection has important theoretical and practical significance.First,the article takes the distribution system configured with SCADA and μPMU as the application scenario,considers that the data of different measurement systems have certain differences,mainly from the three aspects of data accuracy,data time scale,and data upload frequency,performs matching analysis on data from different systems.And the data upload frequency matching method is studied emphatically.Considering the different upload frequency of the two systems,the article analyzes that the load data fluctuation has a linear characteristic under a small time scale,and on this basis,uses Lagrange Interpolation and extrapolation in the segmented interpolation algorithm to supplement the SCADA measurement data that is missing at the time of μPMU data upload.Finally,the two interpolation algorithms are compared and verified through simulation examples.It is believed that using relatively high-precision interpolation algorithms to correct the data of the extrapolation algorithm will significantly improve the accuracy of the obtained SCADA interpolation data.Secondly,based on the data matching,the article conducts the research on the state estimation of distribution network containing SCADA and μPMU system data.Aiming at the characteristics of three-phase unbalanced distribution network,the article constructs a system three-phase line model,and uses the branch current method to build a state estimation equation matrix,and studies and analyzes the state estimation process under data matching.In the analysis of calculation examples,the article uses IEEE33 node calculation examples to verify the above methods,and the data shows that it has a good estimation effect.And on this basis,the article also uses the Python programming language to program the above methods,and the application effect has achieved the expected purpose.Finally,This article takes the middle and low voltage side distribution network equipped with the above three measurement systems as a research scenario.Considering that the smart meter in the low-voltage side AMI system or the medium-voltage side measurement system may also malfunction and cause insufficient or incorrect measurement data.Based on this,the article studies the general law of electricity consumption by low-voltage users,and combines the measurement equipment of the medium-voltage distribution system to analyze in detail the establishment of pseudo-measurement data based on the AMI system on the low-voltage user side.In addition,on the basis of the above analysis,the article studies the method of establishing pseudo measurement data on the medium voltage side by using the superposition method.Then the low-voltage pseudo-measurement and state estimation are combined to develop the low-voltage side state estimation.Regarding the application of calculation examples,the article uses the load data of a year in Slovakia in 1998 to verify the construction method of the pseudo-measurement proposed by the article.The state estimation calculation is carried out by building a 15-node low-voltage distribution network system.The results show the effectiveness of the proposed low-voltage pseudo-measurement construction method.