Open AccessAnomaly Recognition and Correction Algorithm for Big Data of Distribution Network Load
Author(s) -
Tao Huang,
Ziqiang Wang,
Wei Wang,
Qiang Zhang,
Yanwei Chen
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/781/4/042024
Subject(s) - computer science , big data , data mining , anomaly detection , identification (biology) , scheduling (production processes) , load balancing (electrical power) , data collection , data processing , load distribution , algorithm , engineering , mathematical optimization , mathematics , database , statistics , biology , botany , geometry , structural engineering , grid
With the continuous improvement of the intelligent degree of distribution network, load information presents the growth of big data level, large-scale, mixed, inaccurate monitoring or collection of load data often appear, which brings some difficulties to the scheduling and forecasting work. Therefore, it is necessary to identify and correct these abnormal data. Based on the analysis of the causes and distribution characteristics of different types of abnormal load data, this paper identifies and modifies the abnormal data based on the improved density estimation algorithm, so as to realize the rapid processing and repair of the distribution network load big data. Finally, the power load data of a province in 2020 is selected to clean the load data. The results show that the algorithm proposed in this paper can quickly and accurately realize the identification and correction of abnormal load data.