Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Defense of Massive False Data Injection Attack via Sparse Attack Points Considering Uncertain Topological Changes
Author:
Affiliation:

1. School of Electrical Engineering, Guangxi University, Nanning, China
2. Guangxi Power Grid Co. Ltd., China Southern Grid (CSG), Guilin, China

Fund Project:

This work was supported in part by the National Natural Science Foundation of China (No. 51767001).

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    Abstract:

    False data injection attack (FDIA) is a typical cyber-attack aiming at falsifying measurement data for state estimation (SE), which may incur catastrophic consequences on cyber-physical system operation. In this paper, we develop a deep learning based methodology for detection, localization, and data recovery of FDIA on power systems in a coherent and holistic manner. However, the multi-modal probability distributions of both measurements and state variables in SE due to ever-changing operating points and structural/topological changes pose great challenges in detecting and localizing FDIA. To address this challenge, we first propose an enhanced attack model to launch massive FDIA on limited access points. Second, we train an auto-encoder (AE) with a Bayesian change verification (BCV) classifier using N-1 contingencies to detect FDIA with unseen N-k operational topologies. Third, to avoid model collapse caused by multi-modal measurement distribution, an AE-based generative adversarial network (GAN) is derived to generate a diverse candidate set of normal measurement vectors with various operational topologies. Finally, we develop a pattern match algorithm to localize and recover the falsified measurements and state variables by comparing the falsified measurement vectors with the normal measurement vectors in the candidate set. Case studies with IEEE benchmark systems and a modified 415-bus China Southern Grid system are provided to validate the proposed methodology. It shows that the proposed methodology achieves an average 95% accuracy for detection, over 80% accuracy for localization of FDIA, and recovers the measurement and state variables close to their true values.

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History
  • Received:September 30,2020
  • Revised:January 01,2021
  • Adopted:
  • Online: November 21,2022
  • Published: