Research On Calculation Method Of Line Loss In Transformer District Based On Data Mining Technology

With the rapid development of the power system,the research on line loss is getting more and more attention.Line loss rate is an important technical and economic indicator of the power system.It is also a comprehensive technical and economic indicator to measure the business and management level of the power system.Reduceing power consumption is an indispensable enterprise work.Currently the grid,especially the low-voltage distribution network has a great loss reduction space.However,due to the huge structure of low voltage distribution network,many elements and nodes,the necessary operational information and data are difficult to be collected.Line loss calculation results are not accurate because of the lower management level of the transform district and the presence of stealing and leaking electricity behavior.Based on the above situation,putting forward a method to calculate the Line Loss Rate rapidly and accurately has a great significance.To solve existing problems,the author presents a method to calculate the line loss rate in transformer district and realizes the method by programming,which was combined improved K-Means clustering algorithm with BP neural network model optimized by Levenberg-Marquardt(LM)algorithm.Samples were classified by improved K-Means clustering algorithm according to electric characteristics.Thus,the numerical dispersion of line loss rate in transformer district was solved.On this basis,each class was trained by BP neural network optimized by LM algorithm.Variation of transformer district line loss rate was obtained by using BP neural network to map relation between line loss rate and electric characteristic parameters.601 transformer districts with residents load and 580 transformer districts with industrial load in a region as examples,simulation and calculation were performed to verify the accuracy of the proposed method.The results show that the method has the advantages of fast convergence and high accuracy,compared to standard BP neural network.Line loss can be calculated quickly and accurately using the proposed method,and the method can also provid ideas for the analysis of other losses,so it has practical significance.