A False Data Attack Identification Method Based on Probability Distribution

Abstract

The invention is a false data attack identification method based on probability distribution, which belongs to the field of smart grid data information security. The method includes the following steps: reading known grid topology and line parameter information; calculating node admittance matrix; reading measurement data; calculating false measurement data increment according to the attack target to be achieved; Inject the incremental measurement data into the original measurement data to obtain false measurement data; perform probability distribution fitting of measurement changes to identify false data attacks. The cost of the new false data attack method mentioned in the present invention is the same as that of the previous false data construction method, and both can accomplish the attack target well, but the false measurement data of the former can more easily escape the existing bad data identification module. The present invention is an improvement based on the shortcoming of the bad data identification module.

Description

technical field [0001] The invention relates to the field of smart grid data information security, in particular to a method for identifying false data attacks based on probability distribution. Background technique [0002] Modern smart grids use different state estimation algorithms, such as the weighted least squares method, to obtain the best estimate of the current voltage amplitude and voltage phase angle of each power node in the power system from the measurement data containing measurement noise. In addition, the estimated values ​​also include the injected active power and reactive power of each node, the active power and reactive power at the head end of each branch, and the active power and reactive power at the end of each branch. After years of development, the power system has established a relatively complete bad data identification theoretical system, and correspondingly, a bad data identification module has been added to the state estimator, so that the powe...

AI Technical Summary

Problems solved by technology

If the carefully constructed false data is injected into the original measurement data and avoids the existing bad data identification module, the calculation results of the state estimation can be affected or even manipulated

[0003] Although some literatures have proposed some false data construction methods for the AC-DC transmission model of the smart grid and achieved certain results, these false data construction methods do not take into account that the residual error of each measurement before and after the attack has not changed, so the original Those bad data with large residuals will still be identified as bad data and eliminated due to large residuals after false data injection, thus losing the meaning of false data injection

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