Distribution network data online cleaning method based on dynamic outlier detection

Abstract

The invention relates to a distribution network data online cleaning method based on dynamic outlier detection. The distribution network data online cleaning method comprises the steps of: establishing a buffering queue which is used for receiving the first batch of distribution network real-time data set A1, and obtaining the length L1 of the buffering queue; calculating Euclidean distances between all data points to the data set A1, so as to form a Euclidean matrix D1; diluting a Euclidean matrix Dn-1 if the buffering queue continues receiving the nth batch of distribution network real-time data set An, so as to obtain a diluted distribution network real-time data set A'n-1, and merging a Euclidean matrix Dn and a Euclidean matrix D'n-1, so as to obtain a matrix D''n; detecting abnormal data of the D''n by using an outlier detection algorithm, sending wrong data to a data source for retransmission or error correction, outputting cleaning results, and emptying the buffering queue. According to the method provided by the invention, through three processes that the data buffering queue is established, the Euclidean matrices are dynamically updated and abnormal data points are removed, the real-time online distribution network data generated in the operation and failure processes of a distribution network are cleaned, so that the distribution network data quality and availability are improved.

Description

technical field [0001] The invention relates to an online cleaning method for distribution network data, in particular to an online cleaning method for distribution network data based on dynamic outlier detection. Background technique [0002] As the last link of the power system facing users, the distribution network has the characteristics of large amount of equipment and wide distribution, complex and unstable network topology, etc. The information collected by power distribution terminals is mostly state quantities and analog quantities such as current, voltage, and switch positions. The high collection frequency makes the overall collection data volume very large. At the same time, the power distribution automatic system has many services, and the communication requirements of each application are different. Some applications have high requirements for real-time data. In order to realize the unification of data monitoring communication and In order to meet the needs of ...

AI Technical Summary

Problems solved by technology

Although most of the data collected by the distribution network are normal data and only a small amount of abnormal data, this will still interfere with the operation status monitoring, real-time simulation analysis and basic calculation optimization of the distribution network.

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