Abstract

Improving the performance of industrial gas turbines has always been at the focus of attention of researchers and manufacturers. Nowadays, the operating environment of gas turbines has been transformed significantly respect to the very fast growth of renewable electricity generation where gas turbines should provide a safe, reliable, fast, and flexible transient operation to support their renewable partners. So, having a reliable tools to predict the transient behavior of the gas turbine is becoming more and more important. Regarding the response time and flexibility, improving the turbine performance during the start-up phase is an important issue that should be taken into account by the turbine manufacturers. To analyze the turbine performance during the start-up phase and to implement novel ideas so as to improve its performance, modeling, and simulation of an industrial gas turbine during cold start-up phase is investigated this article using an integrated modular approach. During this phase, a complex mechatronic system comprised of an asynchronous AC motor (electric starter), static frequency converter drive, and gas turbine exists. The start-up phase happens in this manner: first, the clutch transfers the torque generated by the electric starter to the gas turbine so that the turbine reaches a specific speed (cranking stage). Next, the turbine spends some time at this speed (purging stage), after which the turbine speed decreases, sparking stage begins, and the turbine enters the warm start-up phase. It is, however, possible that the start-up process fails at an intermediate stage. Such unsuccessful start-ups can be caused by turbine vibrations, the increase in the gradients of exhaust gases, or issues with fuel spray nozzles. If, for any reason, the turbine cannot reach the self-sustained speed and the speed falls below a certain threshold, the clutch engages once again with the turbine shaft and the start-up process is repeated. Consequently, when modeling the start-up phase, we face discontinuities in performance and a system with variable structure owing to the existence of clutch. Modeling the start-up phase, which happens to exist in many different fields including electric and mechanical application, brings about problems in numerical solutions (such as algebraic loop). Accordingly, this study attempts to benefit from the bond graph approach (as a powerful physical modeling approach) to model such a mechatronic system. The results confirm the effectiveness of the proposed approach in detailed performance prediction of the gas turbine in start-up phase.

Highlights

  • Modeling the cold start-up phase of industrial gas turbine is studied in this article

  • An asynchronous AC motor motor controlled by a static frequency converter (SFC) drive exists as electric starter

  • Bond graph models of these components controlled by a SFC drive exists as electric starter

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Summary

Introduction

Current focuses of industrial gas turbine manufacturers are on minimizing costs, enhancing. Current of while industrial gas turbine manufacturers on minimizing costs, companies enhancing flexibility andfocuses capacity, retaining the reliability. This hasare motivated well-known flexibility and capacity, while retaining the reliability. The reason behind in gasunsuccessful transmissionstart-ups line stations countries, resulting in tripthe in increase the turbine [4].gradients. If the turbine is unable attain the speed andof itsexhaust speed falls below a certain in limit, clutch engages with the turbine shafttoonce more self-sustained speed and its speed falls below a certain limit, the clutch engages with the turbine shaft and the start-up process is repeated.

Improving
ImprovedThese start-up process
Description of Gas Turbine and its Start-Up Process
Bond Graph Modeling of Components During Cold Start
Overall
Modeling the Induction Motor and SFC Drive
Modeling the Induction Motor Using Bond Graph
Modeling SFC Drive
Modeling the Industrial Gas Turbine
Completed Model of Gas Turbine in Cold Start-Up
Analysis of Simulation Results
Analysis of Simulation
Simulation Method
20. Extracted
Findings
Conclusion
Full Text
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