Physics-informed neural network enhances power system security against data attacks

Researchers have developed a new Physics-Informed Neural Network (PINN) designed to enhance the security of power system state estimation against false data injection attacks. This model integrates power-flow consistency directly into its learning process, aiming for improved accuracy and robustness without relying on adversarial training methods. The approach utilizes a dynamic loss-weighting formulation to manage the balance between data fitting and physics residuals, showing superior performance compared to existing PINN variants on the IEEE 118-bus system. AI

影响 Introduces a more robust method for securing power grid operations against cyber-physical attacks.

排序理由 This is a research paper detailing a novel approach to a specific problem in power systems using neural networks.

在 arXiv cs.LG 阅读 →

• arXiv
• False Data Injection Attacks
• IEEE 118-bus system
• Physics-Informed Neural Network
• Power System State Estimation

• 论文
• 安全

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