Standardized Digital Twin Framework for Modeling, Monitoring, and Detection of Cyber–Physical Threats in Photovoltaic Plant Sensors

This paper presents a cost-effective and real-time Digital Twin (DT) framework for cyber-physical anomaly detection, monitoring, and modeling in photovoltaic (PV) plants. The proposed approach employs open-source tools, such as OpenModelica, and integrates the Functional Mockup Interface (FMI) standard to create portable, scalable, and interoperable Functional Mock-up Units (FMUs). These FMUs serve as dynamic virtual representations of the physical system, enabling continuous estimation, tracking, and recognition of abnormal behavior in real time. The framework is validated using a Hardware-in-the-Loop (HIL) platform that emulates realistic PV plant operations, providing a controlled environment for testing and verification. Additionally, the DT runs on a low-cost Single Board Computer (SBC), comparing real-time sensor measurements with model-based estimations to accurately detect anomalies without requiring extensive computational resources. For effective data analysis and visualization, the system uses open-source time-series database solutions, facilitating early detection and traceability of cyber-physical threats. By integrating measurement and monitoring functions with behavioral modeling and anomaly detection into a unified, standardized framework, this work supports the detection and analysis of cyber-physical security threats in PV plant sensors, contributing to more reliable, efficient, and accessible system monitoring.