The EPRI Gas Turbine Digital Twin – a Platform for Operator Focused Integrated Diagnostics and Performance Forecasting

EPRI has been developing a digital twin of simple and combined cycle gas turbines over the last 5+ years to provide owners and operators with improved capabilities that typically reside in the expert domain of OEMs and 3rd party service providers. The digital twin is a digital model, a physics-based representation of the actual asset. The model is thermodynamic and is created with the intent to support 5 M&D areas: • Integrate with existing M&D tools such as advanced pattern recognition (APR) • Power plant performance prediction and trending such as day, week, and month ahead performance prediction for capacity and generation planning • Health Monitoring and Fault Diagnostics to support asset management with additional health scores and virtual instrumentation enabled by the digital twin model • Monitoring and prediction of both base and part-load performance. Many gas turbine tools have been simplified to work only at full load conditions. To be useful and to improve utilization of collected data, part-load conditions should also be considered. • Outage and repair impacts, including “what-if” capability to understand and quantify potential root causes of less than expected performance improvement or recovery after outage and repairs. This paper presents current progress in creating an EPRI Digital Twin applicable to gas turbines. The formulation, methodology, and real-world use cases are presented. To date, digital twins have been created and tested for both E and F class frames. This paper describes the process of generating closed-form equations capable of transforming existing, measured historian data into the health parameters and virtual sensors needed to better track unit health and monitor faulted performance. These equations encapsulate the digital twin physical model and provide end-users with a methodology to calibrate to their specific unit and efficiently use their choice of monitoring software. Tests have been performed using operator data and have shown good accuracy at detecting anomalous operation and predicting week ahead performance with excellent accuracy. Post-outage impact analysis is also assessed. Real-world application cases for the digital twin are also presented. Examples include using the digital twin to identify causes of post-outage emissions and performance issues, expected impact of degradation and fault conditions, and simulating improvements to operation through part repair and upgrades.