Operating Data

Amidst the energy transition, gas turbines are vital for grid stability, supplying flexible, efficient power to support renewable sources. Advanced anomaly detection is key to ensuring their reliability and safeguarding power generation's efficiency, safety, and economic viability. Our study presents a data-driven-based digital twin model that transforms gas turbine anomaly detection with a comprehensive, dynamic solution covering all turbine components. The results showcase the model's exceptional ability to identify anomalies across various operating conditions, highlighting its potential to enhance operational efficiency and reliability.