The electric power grid is realizing tremendous growth in the integration of technology-enabled solutions to improve system performance, reduce costs related to both operational and life-cycle maintenance, reduce environmental impact, improve the fidelity and accuracy of measurements and monitoring, integrate renewable energy and associated energy resources, and improve overall reliability. These technology advances collectively increase the attack surface of the electric power grid ecosystem. As the level of automation in critical energy infrastructure increases, the ability to detect cyber intrusions and attacks becomes even more critical as well as challenging. Coordinated cyberattacks can maximize damage by making use of multiple grid control techniques. One approach to prevent such attacks is to continuously compare grid state time series sensor measurements with continuously running real-time simulations to detect unexpected deviations early in the cyberattack process. This presentation will discuss a Digital Twin Framework (DTF) implementation designed specifically to prevent such attacks. The DTF and model implementation are validated through comparison of experimentally collected data from a scaled three-phase transmission system. The hardware sensor data and controls are provided by embedded controllers which support standard IP communications. Results indicate that the DTF implementation presented here performs well and extends current DT capabilities in the areas of modularity, interoperability, and running in real-time along-side the cyber-physical system parent.
