A Digital Twin Environment Designed for the Implementation of Real Time Monitoring Tool

Real time models and digital twin environments represent a new frontier that allow the development of supplemental data analytics of measurable and unmeasurable parameters for a variety of power plant configurations. Performance prediction, monitoring of degradation effects, and a faster recognition of anomalous events during power plant load following operations and/or due to cyber-attacks can be easily detected with the support of digital twin environments. In the research work described in this article, a digital twin environment was developed to capture the dynamics of a micro compressor-turbine system modified for hybrid configuration at the Department of Energy’s National Energy Technology Laboratory (NETL). The innovative approach for the development of the digital twin environment was based on creating a compressor-turbine physics-based model using a stateless methodology generally utilized for microservices architectures. The advantage of using this approach was represented by modeling individual or a group of power plant components on distributed computational resources such as clouds in a lightweight and interchangeable manner. Supplemental data analytics were performed using an online system identification tool developed in previous work and applied to an unmeasurable parameter only available in the digital twin system. This work demonstrated the ability to train a recursive algorithm to predict a supplemental parameter for faster anomaly detection or for replacing the physics-based model during design or monitoring of operational systems. The thermodynamic compressor-turbine net power was the unmeasurable parameter only available in the digital twin model, which was predicted with the online system identification tool. Results showed that the online system identification algorithm predicted the dynamic response of the thermodynamic net power based on a set of experimental data points at nominal operating conditions with an absolute mean percentage error of ∼0.644%.

Development of Real-Time System Identification to Detect Abnormal Operations in a Gas Turbine Cycle

This paper presents a novel online system identification methodology for monitoring the performance of power systems. This methodology was demonstrated in a gas turbine recuperated power plant designed for a hybrid configuration. A 120-kW Garrett microturbine modified to test dynamic control strategies for hybrid power systems designed at the National Energy Technology Laboratory (NETL) was used to implement and validate this online system identification methodology. The main component of this methodology consists of an empirical transfer function model implemented in parallel to the turbine speed operation and the fuel control valve, which can monitor the process response of the gas turbine system while it is operating. During fully closed-loop operations or automated control, the output of the controller, fuel valve position, and the turbine speed measurements were fed for a given period of time to a recursive algorithm that determined the transfer function parameters during the nominal condition. After the new parameters were calculated, they were fed into the transfer function model for online prediction. The turbine speed measurement was compared against the transfer function prediction, and a control logic was implemented to capture when the system operated at nominal or abnormal conditions. To validate the ability to detect abnormal conditions during dynamic operations, drifting in the performance of the gas turbine system was evaluated. A leak in the turbomachinery working fluid was emulated by bleeding 10% of the airflow from the compressor discharge to the atmosphere, and electrical load steps were performed before and after the leak. This tool could detect the leak 7 s after it had occurred, which accounted for a fuel flow increase of approximately 15.8% to maintain the same load and constant turbine speed operations.