Distributed Snitch Digital Twin-Based Anomaly Detection for Smart Voltage Source Converter-Enabled Wind Power Systems

TL;DR

This paper introduces a Snitch Digital Twin architecture for wind farm cyberattack detection, offering improved accuracy, speed, and robustness over existing AI-based methods in smart grid systems.

Contribution

The paper presents a novel digital twin-based framework for real-time anomaly detection in wind power systems, enhancing detection capabilities against stealthy cyberattacks.

Findings

Improved attack detection accuracy in simulations.

Faster response times compared to AI-based methods.

Enhanced robustness under various attack scenarios.

Abstract

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.