Lean Analysis Of Transformer District Line Loss Based On Data Mining Applied Research

Transformer district line loss rate is an important index for accuratly measuring power management level and business level.Traditional low-voltage station area line loss management adopts the general standards of the State Grid,and defines the qualified interval of line loss in the station area at the same time as between 0 and 10%.This one-size-fits-all judgment method is relatively extensive,and does not fully consider the situation of different stations.There are certain limitations and one-sidedness.How to use the massive low-voltage station area data to accurately identify the characteristics of the station area and divide the station area category to achieve classification management is of great significance to promote the lean management of the station area lineThis article firstly analyzed the related theories of station area line loss and traditional line loss management methods,systematically summarized the factors that affect station area line loss rate and the problems existed in the traditional management mode of station area line loss,sorted out the relevant policy requirements,management methods and management effects of the line loss in transformer district line loss,proposed the direction of lean management of technical line loss,and deepened the application of data mining technology in the line loss management in transformer district line loss.Secondly,using big data technology to analyze the line loss characteristics of transformer district line loss,apply data mining and other related technologies and methods to the massive station area data,guided by the theory of hierarchical clustering and splitting method,the K-means algorithm is applied to each layer of clustering operation,combined with the principles and methods of mathematical statistical analysis,deeply digging and using historical station area data information,exploring and creating a clustering model suitable for the classification of low-voltage station area of the power grid and calculation method of line loss rate interval.On this basis,a reasonable interval model of station area line loss based on big data mining technology was constructed,and used to dynamically monitor station area line loss data,which effectively promoted more accurate station area governance and improved the recognition of abnormal station areas.Finally,the specific implementation step of clustering modeling was described in detail taking the station data in the area under the jurisdiction of a provincial power company in central China as an example,and the results wereanalyzed.The results showed that the application of a reasonable analysis model based on the historical line loss data in the station area can improve the efficiency of the station area anomaly investigation by more than 50%,shorten the average investigation time by more than 80%,and effectively improved the abnormality of the line loss.The number of standard-compliant stations has increased significantly.The qualified rate of line loss has increased from 95.83%to 98.16%,and the number of unqualified stations has gradually decreased.The comprehensive line loss rate has been reduced from 3.83% to 3.15%,which has effectively improved the management level of the station,and leafily management was realized.