False data attack identification method based on probability distribution

Abstract

The invention relates to a false data attack identification method based on probability distribution, and belongs to the field of intelligent power grid data information security. The method comprisesthe following steps: reading a known power grid topological structure and line parameter information; calculating a node admittance matrix; reading measurement data; calculating a false measurement data increment according to an attack target needing to be achieved; incrementally injecting the false measurement data into the original measurement data to obtain false measurement data; and carryingout measurement variation probability distribution fitting, and identifying false data attacks. Compared with the conventional false data construction method, the novel false data attack method provided by the invention has the advantages that the required cost is consistent, an attack target can be well completed, but the false measurement data of the provided false data construction method canavoid the existing bad data identification module more easily. The method is an improvement provided based on the defect 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 false data attack identification method 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 estimates 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 value also includes the injected active power and reactive power of each node, the head-end active power and reactive power of each branch, and the end active power and reactive power of each branch. After years of development, the power system has established a relatively complete theoretical system of bad data identification. Correspondingly, a bad data identification module has been added to the state estimator, so that the power grid regulators ...

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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