Power distribution network bad data identification method based on mass tags

Abstract

The invention discloses a power distribution network bad data identification method based on mass tags, and belongs to the field of power system calculation and analysis. Under the condition that multi-source data are acquired, corresponding data mass scores are evaluated through constraint rules of six influence factors such as voltage amount measurement U, current amount measurement I, active amount measurement P, active electricity measurement and reactive electricity measurement, mass tag values are calculated through the mass scores measured by the amounts after evaluation is finished, and bad data are identified according to the calculation result. The defect that in a traditional data identification method, local data errors are shared to all nodes is overcome, redundancy measured data of an upper voltage grade and a lower voltage grade are comprehensively utilized, in combination with the mass tags, the bad data are identified, and the quality of power distribution network virtual measurement and state estimation input data is improved.

Description

technical field [0001] The invention relates to a method for identifying bad data of a distribution network based on a quality label, and belongs to the field of power system calculation and analysis. More specifically, through a distribution network data identification method based on quality labels, it is a practical data identification method to detect and identify bad data in the distribution network. State estimation provides data support. Background technique [0002] In the past, the distribution network lacked data measurement equipment and monitoring means. In this case, the data quality identification of the distribution network was often transformed into a power flow matching problem based on a series of assumptions. With the continuous implementation of power system automation, there are new requirements for the quality identification of distribution network data. Distribution network data quality identification requires two types of data, namely measurement da...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a method for identifying bad data in distribution networks based on quality labels. In view of the current distribution automation pilot area measurement configuration and the current status of multiple data sources, the measurement data of the distribution network can be identified before estimation. It overcomes the shortcomings of the traditional data identification method that the local data errors are spread to all nodes, and comprehensively utilizes the redundant measurement data of the upper and lower voltage levels, combined with the quality label, to identify bad data and improve the virtual quantity of the distribution network. Quality of input data for measurement and state estimation