Distributed Snitch Digital Twin-Based Anomaly Detection for Smart Voltage Source Converter-Enabled Wind Power Systems
This addresses cyberattack detection for distributed energy systems, offering a domain-specific solution that appears incremental as it builds on digital twin concepts for a specific application.
The paper tackles the problem of detecting cyberattacks in smart grids by proposing a Snitch Digital Twin architecture for wind power systems, which improves detection accuracy, response time, and robustness compared to existing methods like ANNs and DRL.
Existing cyberattack detection methods for smart grids such as Artificial Neural Networks (ANNs) and Deep Reinforcement Learning (DRL) often suffer from limited adaptability, delayed response, and inadequate coordination in distributed energy systems. These techniques may struggle to detect stealthy or coordinated attacks, especially under communication delays or system uncertainties. This paper proposes a novel Snitch Digital Twin (Snitch-DT) architecture for cyber-physical anomaly detection in grid-connected wind farms using Smart Voltage Source Converters (VSCs). Each wind generator is equipped with a local Snitch-DT that compares real-time operational data with high-fidelity digital models and generates trust scores for measured signals. These trust scores are coordinated across nodes to detect distributed or stealthy cyberattacks. The performance of the Snitch-DT system is benchmarked against previously published Artificial Neural Network (ANN) and Deep Reinforcement Learning (DRL)-based detection frameworks. Simulation results using an IEEE 39-bus wind-integrated test system demonstrate improved attack detection accuracy, faster response time, and higher robustness under various cyberattack scenarios.