Session: 5.1 - Advanced Tools for Cyber-Physical Systems and Digital Twins

Paper Number: 118651

118651 - Gas Turbine Digital Twin and Use Cases 

Digital Twins have transcended the realm of mere buzzwords and have become an indispensable tool for various industries, including the power sector. A true digital twin has the ability to analyze historical data, mimic real-time operation, and predict future capabilities, thereby significantly enhancing operational efficiency and decision-making. The Gas Turbine R&D team at the Electric Power Research Institute (EPRI), in collaboration with partners from Turbine Logic, has accumulated extensive experience in building, testing, and implementing digital twins for gas turbines. This presentation aims to inform the audience about the power and potential of digital twin technology, with a special focus on specific use cases in the gas turbine industry.

One such use case is outage benchmarking, where digital twins facilitate the determination of performance losses or gains across major maintenance events. By leveraging historical and real-time data, digital twins enable power plants to optimize their maintenance schedules, reduce downtime, and minimize costs. Furthermore, digital twin technology allows power plant operators to assess the impact of various maintenance strategies on overall performance, which aids in decision-making and resource allocation.

Another vital use case for digital twins in the gas turbine industry is firing temperature estimation. Accurate firing temperature estimates are crucial for determining the optimal operating conditions of a gas turbine, as they influence overall performance, efficiency, and the life span of turbine components. By integrating data from sensors, control systems, and physics-based models, digital twins provide a more accurate and reliable estimation of firing temperatures than traditional methods. This, in turn, leads to improved performance and reduced operational risks.

Compressor washing is an essential maintenance procedure to ensure the optimal performance of gas turbines. The use of digital twins in this process allows power plant operators to determine the optimal frequency and effectiveness of compressor washing. By simulating various washing scenarios and analyzing their impact on performance, digital twins provide valuable insights that help operators maximize the benefits of compressor washing while minimizing its costs and environmental impact.

Lastly, digital twins play a significant role in the performance characterization of gas turbines. By creating a virtual representation of a gas turbine, digital twins allow operators to monitor performance metrics, such as efficiency, emissions, and reliability, under different operating conditions. These insights enable power plant operators to optimize their turbine operations and identify potential issues before they escalate into costly failures.

In conclusion, the adoption of digital twin technology in the gas turbine industry has proven to be an invaluable tool for enhancing operational efficiency, reducing costs, and mitigating risks. Through the use cases of outage benchmarking, firing temperature estimation, compressor washing, and performance characterization, digital twins offer unparalleled opportunities for power plant operators to harness the full potential of their assets. As the technology continues to advance, it is expected that the applications and benefits of digital twins in the power sector will only continue to expand, paving the way for a more sustainable and efficient future.

Presenting Author: David Noble EPRI

Presenting Author Biography: Bobby Noble is the Program Manager for the Gas Turbine R&D at EPRI. He has more than 19 years of experience in the gas turbine and power industries. His primary gas turbine expertise is in experimental gas turbine combustion research, with focus on combustion dynamics, instrumentation and diagnostics, high-hydrogen and alternative fuels, and next-generation, low-NOx combustor architectures. He holds three patents and has authored/co-authored a renewable fuels book, 14 journal publications, and 47+ conference publications.

Bobby is an ASME Fellow and received a bachelor of science degree in mechanical engineering from Clemson University in 2003. He received his master of science degree in aerospace engineering from the Georgia Institute of Technology in 2006.