Abstract
In recent years, digital twin models have gained significant interest as powerful tools for predicting the behavior of complex processes and types of equipment, assisting in damage prevention, increasing productivity, and facilitating decision-making in various industries. This study focuses on developing a digital twin of a specific gas turbine for power forecasting by accurately modeling its behavior under changing ambient conditions.
Despite its promising task, it comes with equal challenges: to generate a precise digital twin needs to start with an accurate model. From this point forward, the digital twin can correctly simulate the behavior and assist the industry with decision-making challenges.
A comprehensive gas turbine model was developed using Python, enabling the estimation of power generation based on varying ambient temperatures. The model underwent verification and validation procedures to ensure its accuracy and reliability. Furthermore, it was integrated with a weather forecast API, allowing the prediction of the gas turbine's power output under different weather scenarios.
The methodology employed in building the digital twin model is described in detail, including the equations utilized and the setup for the simulation software. The verification and validation process is thoroughly discussed to emphasize the credibility of the digital twin model for real-world applications.
The results obtained from the digital twin simulations demonstrate its potential capability to provide precise and reliable power generation forecasts for the studied gas turbine.
This thesis contributes to the advancement of digital twin models for gas turbines and showcases the potential of such models in power forecasting and decision-making in the energy industry. The outcomes also emphasize the importance of accurate modeling and data integration to maximize the benefits of digital twin technology for industrial applications.