A Method for Identification of Active Bad Data Based on DC Power Flow Model

Abstract

The invention discloses a direct-current power flow model based active power bad data identification method. The method comprises: based on a direct-current power flow model, selecting different active power measurement combinations to calculate values of state variables to obtain multiple groups of state variables; then performing clustering analysis on the state variables; and quickly identifying active power bad data according to a clustering result. The method disclosed by the invention has the characteristics of small calculation amount, easiness for parallelization and high identification correctness, thereby being suitable for performing online preprocessing on measurement before state estimation.

Description

technical field [0001] The invention belongs to the technical field of electric power systems, and in particular relates to a method for quickly identifying bad active power data by obtaining multiple groups of state quantities through measurement combination calculation, and then performing cluster analysis on them. Background technique [0002] Bad measurement data, also known as bad data, refers to data that deviates far from the real change trajectory. The existence of bad data may lead to the contamination of state estimation results. Bad data detection and identification is to judge whether there is bad data in a certain quantity measurement, and determine which quantity measurements are bad data. At present, the main methods of bad data identification are residual search method, method based on non-quadratic criterion estimation and quantitative measurement mutation detection method. The residual search method can generally only identify a single bad data, and requir...

AI Technical Summary

Problems solved by technology

The residual search method can generally only identify a single bad data, and requires multiple iterations of state estimation, which has poor real-time performance; the method based on non-quadratic criterion estimation is very empirical, and generally can only identify a single bad or weak correlation It is not an optimal estimate because the adverse effects cannot be completely ruled out; the premise that the measurement mutation detection method is effective is that the power network structure at the adjacent sampling time is unchanged, and the bad data in the measurement data at the previous sampling time Data has been detected and corrected

[0003] At present, most of the existing research is based on the residual, which belongs to the identification after estimation, and there are few studies on the identification technology before estimation.

Images