Issue link: https://resources.mouser.com/i/1442808
13 4.0 Using IoT sensor data, DTs can be used to predict the plant's performance, reliability, and wear over time. They also help to enhance the plant's efficiency by evaluating different scenarios and understanding tradeoffs. As the power plant continues to operate, the DTs also progressively improve their ability to model and track the state of the plant. This dynamic state modeling, coupled with the ability to test "what- if" scenarios against business objectives, gives plant operators enough visibility to make informed decisions regarding performance versus asset life. They can optimize the instantaneous and transient control of the plant for efficiency or higher performance levels and to accurately schedule loads, lineups, and maintenance windows. The given DT provides the power dispatcher with visibility into the bigger picture of the operations of an asset within a time scale. As such, the dispatcher can make calculated commitments to dispatch energy without worrying about unforeseen maintenance or wasted fuel (Figure 1). Enabling Technologies The leading enabling technologies for DTs used in power plants/ fleets include: Physics-Based Models Deep physics-based models imitate the flow, thermal, combustion, and mechanical aspects of the power equipment. Artificial Intelligence Artificial intelligence (AI) algorithms such as pattern recognition, learning models, unstructured data analytics, multi-modal data analytics, and knowledge networks provide a deeper understanding of the operating environments. Sensing Technology Advancements in low-power sensors with small footprints, which are designed to work under harsh and demanding environments, generate the "big data" used in industrial DTs. Printed sensors, inspection technologies, atmospheric/weather data, and plant component analytics are some examples. How Digital Twinning Works DTs mostly carry a deep domain knowledge of specific industrial assets from which the twins can display sophisticated models or a system of models. A massive amount of design, manufacturing, inspection, repair, online sensor, and operational data feeds each DT. Using a collection of computational physics-based models and Figure 1: A digital twin provides an inside look into an individual power equipment's internal conditions, functions, and behavioral patterns in real-time. (Source: GE Digital)