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Identification and Detection of Cyber-Attack on Smart Grid Using Artificial Intelligence

Ganjkhani, Mohammad | 2022

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 55026 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Abbaspoor Tehrani Fard, Ali
  7. Abstract:
  8. The purpose of this study is to identify the False Data Injection (FDI) attack and reconstruct the incorrect data created by FDI in the power system using machine learning algorithms. Unlike conventional power grids, the smart grids due to the increase of smart devices and communication networks to transfer power grid information from one point to another and the need to control and monitor the power grid is an electrical network that is integrated with a communication network (cyber-physical system). A communication network that transmits data between the control center and smart meters increases the threat of cyber-attacks in the power grid. In this study, the aim is to investigate the FDI in the power grid and detect it using the artificial neural network and auto-encoder. This threat can be accompanied by changes in the values of the power grid state variable, which, if FDI is successfully launched, will lead to power outages and system blackouts, faulty operation of power systems such as inadequate price in the real-time electricity market, system instability. In this study, it is shown that the mentioned machine learning algorithms can show good performance against this anomaly and distinguish the normal state of the power grid from its abnormal state. Finally, by variational auto-encoder algorithm, the data that has been deviated from its normal value and modified is returned to its normal value and the reconstructed data can be used in the operation of the power system. The used network in this simulation is IEEE-14, 30, and 118 nodes test system
  9. Keywords:
  10. Smart Power Grid ; Variational Autoencoder ; Autoencoder Neural Networks ; Artificial Neural Network ; State Estimation ; Power Systems ; Normal Data Classification ; False Data Classification ; Manipulated Data Reconstruction ; False Data Injection

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