Cyber-Physical Systems and Power Grid Resilience

The field of cyber-physical systems is witnessing significant advancements in fault diagnosis, state estimation, and attack detection. Researchers are developing innovative methods to identify and mitigate faults in complex systems, such as those used in autonomous vehicles and power grids. Additionally, there is a growing focus on enhancing the resilience of power systems, including the development of robust state estimation techniques and dynamic voltage control schemes. Notable papers in this area include:

  • A Compositional Approach to Diagnosing Faults in Cyber-Physical Systems, which presents a novel approach to diagnosing faults in cyber-physical systems using assume-guarantee contracts.
  • Robust Power System State Estimation using Physics-Informed Neural Networks, which proposes a hybrid approach to enhance the accuracy and robustness of power system state estimation using physics-informed neural networks.
  • Detection of Intelligent Tampering in Wireless Electrocardiogram Signals Using Hybrid Machine Learning, which demonstrates the effectiveness of hybrid machine learning models in detecting tampering in wireless electrocardiogram signals. These developments have significant implications for the reliability and security of critical infrastructure, such as power grids and transportation systems.

Sources

A Compositional Approach to Diagnosing Faults in Cyber-Physical Systems

Real-time monitoring of the SoH of lithium-ion batteries

Robust Power System State Estimation using Physics-Informed Neural Networks

Detection of Intelligent Tampering in Wireless Electrocardiogram Signals Using Hybrid Machine Learning

Voltage Regulation in Distribution Systems with Data Center Loads

Dual State-space Fidelity Blade (D-STAB): A Novel Stealthy Cyber-physical Attack Paradigm

Computing Euler products and coefficients of classical modular forms for twisted L-functions

Approximating Euler Totient Function using Linear Regression on RSA moduli

Coordinated Fast Frequency Regulation in Dynamic Virtual Power Plants via Disturbance Estimation

A Single-Point Measurement Framework for Robust Cyber-Attack Diagnosis in Smart Microgrids Using Dual Fractional-Order Feature Analysis

Understanding Malware Propagation Dynamics through Scientific Machine Learning

Identifying the Smallest Adversarial Load Perturbations that Render DC-OPF Infeasible

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