A CGAN-based adversarial attack method for data-driven state estimation - Wang, Qi; Zhang, Jing; Hu, Jianxiong; Wu, Shutan; Hou, Shiyi; Tang, Yi (International journal of electrical power & energy systems, 2025)
•Security threats for power system state estimation problem are analyzed, especially cyber-attacks oriented to data-driven state estimation methods.•Adversarial attack methods for power system state estimation, as well as injection of adversarial samples, are analyzed. Several useful conclusions for practical applications have been drawn.•A CGAN-based adversarial attack method for data-driven state estimation is proposed and optimized to reveal security threats in power systems. Comparison with existing work and other methods demonstrates the superiority and effectiveness of proposed method. With the development of data science and technology, the data-driven algorithms under the new power system architecture provide support for real-time and accurate state estimation (SE). However, the adoption of data-driven algorithms also introduces new security risks to power systems due to their inherent limitations, such as lack of interpretability and poor robustness. Their sensitivity to small perturbations and the limitation of generalization ability make them vulnerable to adversarial attacks. Therefore, to reveal the security threats of adversarial attacks, a conditional generative adversarial network (CGAN) is innovatively adapted to the attack vector generation scenario of the power system in this paper. The feature decoupling and parameter-guided generation capabilities of the model are leveraged to produce adversarial samples that are both covert and threatening. In the proposed CGAN-based adversarial attack method, attackers can execute effective attacks by accessing grid measurements, without requiring any prior knowledge or deep understanding of the system. Simulation results demonstrate that the proposed method could compromise the state estimation process while evading bad data detection, all with reduced attack costs. This enables attackers to manipulate the output of the state estimation model according to their intentions, which represents an improvement over previous work. Comparative analysis with other traditional adversarial attack methods further indicates its effectiveness, thereby uncovering critical security vulnerabilities in data-driven state estimation algorithms.