A Power System Data-Driven State Estimation Adversarial Attack Method Based on Conditional Generative Adversarial Network

Data-driven methods based on artificial intelligence technology exhibit the ability to process data quickly and accurately, thus currently being widely applied in the field of power system state estimation (SE). However, recent studies have found that processing data during the training phase of data-driven algorithms can mislead the operational results, demonstrating security risks in data-driven methods. In light of this, to reveal potential security issues in data-driven algorithms, this paper proposes an adversarial attack method based on conditional generative adversarial network (CGAN). Experimental results indicate that adding minute disturbances generated by CGAN to target samples can misleads the predictions of SE model. This method directs the attack based on preset label conditions during the model application.