Enhancing Cybersecurity of a Hydroelectric Power Plant Through Digital Twin Modeling and Explainable AI

Hydroelectric power plants (HEPPs) are vital components of the renewable energy infrastructure, making their operational security and efficiency critical. HEPPs face increasing vulnerability to cyber threats, which can disrupt operations and compromise energy production. This study investigates the integration of Digital Twin (DT) technology with Explainable Artificial Intelligence (XAI) to improve cybersecurity and anomaly detection in a HEPP located in Türkiye. The DT model simulates key operational parameters, such as water flow, mechanical power, and turbine speed, enabling real-time monitoring, optimization, and secure cybersecurity testing. Simulated cyberattacks on the DT have revealed vulnerabilities in the Modbus protocol, while SHapley Additive exPlanations (SHAP) analysis, an XAI technique, clarifies the influence of operational parameters on anomaly detection outcomes. Gravity with a SHAP value of 0.0001 and water density with a SHAP value of 0.0018 have been identified as the least influential parameters, suggesting limited variability or impact on the detected anomalies. These findings enable the prioritization of critical variables while reducing unnecessary monitoring efforts. The proposed integration improves the accuracy of anomaly detection, enables precise vulnerability identification, and mitigates operational risks without impacting real-world systems. The results demonstrate the effectiveness of the model in detecting anomalies and strengthening the resilience of the system against cyber threats. This approach also provides actionable insights to optimize operational processes, ensuring secure and efficient energy production. The study highlights the transformative potential of DT-XAI integration in advancing the sustainability, security, and resilience of critical energy infrastructures.