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Modeling and Analysis of Heavy-Duty Gas Turbine Based on Frequency Dependent Model
In the last decades, gas turbines (GTs) were developed quickly and used in many applications especially in power plants, due to their reliability, availability, adaptability, fast start capability and short delivery time. Industrial heavy-duty gas turbines (HDGT) are, especially, designed for power...
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Main Authors: | , |
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Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
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Summary: | In the last decades, gas turbines (GTs) were developed quickly and used in many applications especially in power plants, due to their reliability, availability, adaptability, fast start capability and short delivery time. Industrial heavy-duty gas turbines (HDGT) are, especially, designed for power generation. They are specified with long life and higher reliability compared with other types of gas turbines. Generally, the gas turbine is associated with thermal system and electrical system. Thus, the dynamic interactions between the gas turbine and coupled systems require a high accuracy in simulation models of gas turbines. Therefore, increasing attention in the study of heavy-duty gas turbines models has been acquired with diverse amounts of difficulty and merit. Thus, modeling and simulation of the behavior of HDGT under accurate operating conditions play a significant role for efficient design as well as reliable manufacturing practice. In this aspect, several gas turbine models have been developed based on the different parts of heavy-duty gas turbine: the speed governor, the fuel system, the combustion chamber, temperature control and the turbine. This paper presents modeling and analysis of the behavior of heavy-duty gas turbine to understand the power system problems. This is achieved by a complementary and analysis of the frequency dependent model (FD), using Matlab/Simulink. In our approach, simple time delays are integrated to the FD model, taking into consideration the effectiveness and accuracy of the model. |
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ISSN: | 2150-3923 |
DOI: | 10.1109/ICEE49691.2020.9249948 |