Abstract
Data-based decisions have been being made in various fields due to the development of sensors throughout the industries. Likewise, in the power system field, data-based decisions are being made in various tasks, including establishing distribution investment plans. However, in order for it to have validity, it is necessary to get rid of abnormal data or data with low representativeness of a temporary nature. Although in general, such a series of processes are done by preprocessing, the those of power system data should be handled not only noise but also data fluctuations caused by temporary change in operations such as load transfers, as mentioned above. In addition, the characteristics of load data of distribution lines (DLs) can be different depending on the characteristics of the load itself, the characteristics of the connected DLs, and regional characteristics of each DLs, so it is essential to propose and apply the optimized preprocessing method for each DL. In this study, therefore, an optimal preprocessing algorithm for each DL was proposed by mixing standard pattern calculations and polynomials based statistical method, and its appropriateness was verified by comparing the results with actual load transfer records. As a result of the verification, it was confirmed that the load transfer detection accuracy of the proposed method was 88.89%, and the maximum load of the target DL can be reduced up to 11.59% by removing the load transfer data.