Robust data-driven adversarial false data injection attack detection method with deep Q-network in power systems

• Main Authors: Ran, Xiaohong, Tay, Wee Peng, Lee, Christopher Ho Tin
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering; Adversarial examples; Deep reinforcement learning
• Online Access: https://hdl.handle.net/10356/176345
• Institution: Nanyang Technological University

Electric power systems have been increasingly subjected to false data injection attacks (FDIAs) and adversarial examples, which inject well-designed disturbance signals into the measurements, and thereby generate erroneous state estimation (SE) results. The present work addresses this issue by propo...