A new method for detection of fake data in measurements at smart grids state estimation

In the smart grids area, attractive issues are created for the hackers such that they tend to interface the control of smart grids deliberately or unintentionally so that the normal operations of these systems are endangered. According to the insight of the smart grids systems, it is anticipated that in near future, all the distribution systems would be operated smartly. In this regard, the state estimation and control algorithms should be improved to be able to detect and omit fake data of measurements appropriately. Some methods for state estimation, such as Kalman filter as a proper criterion can omit the noisy data from the measurements. Similarly, the chi-square method is capable of detecting bad data, but cannot detect and remove unreal and fake data. The purpose of this work is to propose a suitable method for state estimation in the smart grids considering the possibility of having fake data measurement. It will be shown that the use of evolutionary algorithms, such as bat algorithm with a hybrid approach based on the weighted least square technique makes it possible to detect and omit fake data.