This contribution reports on development of *Performance Digital Twin* for industrial *Small Gas Turbines*. The objective of this study was development of automation systems with control and monitoring functionalities, capable to address the requirements of future gas turbine plants for increased, availability and reliability by use of *Digital Twin* technology.

The project explored development of *Performance Digital Twin* based on *Real-Time Embedded* computing, which can be leveraged with *IOT Cloud Platforms*. Proposed solution was provided in a form of modular software for a range of hardware platforms, with corresponding functionalities to support advanced control, monitoring, tracking and diagnostics strategies.

The developed *Digital Twin* was designed to be used in off-line mode to assist software commissioning process and in on-line mode to enable early detection of degradation and fault modes typical for gas path components. The *Performance Digital Twin* is based on dynamic gas turbine model which was augmented with Kalman tuner to enable performance tracking of physical assets.

To support heterogeneity of gas turbine *Distributed Control Systems* this project explored deployment of *Digital Twin* on multiple platforms, and in the paper, we discuss model-based design techniques and tools specific for continuous, discrete and hybrid systems.

The hybrid solution was deployed on PC based platform and integrated with engine *Distributed Control System* in the field. Monitoring of gas turbine *Performance Digital Twin* functionalities has been established via remote monitoring system *STA-RMS.* Assessment of deployed solution has been carried out and we present results from the field trial in this paper.

The discrete solution was deployed on a range of PLC platforms and has been tested by integrating *Digital Twin* in virtual engine *Distributed Control System* network. The *Performance Digital Twin* was embedded in Single Master PLC and Master-Slave PLC configurations, and we present results from the system testing using virtual gas turbine assets. The *IoT Platform MindSphere* was integrated within virtual engine network, and in this contribution, we explored expansion of the developed system with *Cloud* based applications and services.


Gas Turbine Performance Digital Twin for Real-Time Embedded Systems

Division / Sponsor:
International Gas Turbine Institute (IGTI)

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2020/volume/84058

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