• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.483-494
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• 2023

Recently, gas turbine generators are widely used for frequency control of power systems. Although the inlet temperature of a gas turbine is a key factor related to the performance and lifespan of the device, the inlet temperature is not measured directly for reasons such as the turbine structure and operating environment. In particular, the inlet temperature of the reheating gas turbine is very important for stable operation management, but field workers are experiencing a lot of difficulties because the manufacturer does not provide information on the calculation formula. Therefore, in this study, we propose a method for estimating the inlet temperature of a gas turbine using a machine learning-based linear regression analysis method based on a polytropic process equation. In addition, by proposing an inlet temperature calculation algorithm through the usefulness analysis and verification of the inlet temperature calculation model obtained through linear regression analysis, it is intended to help to improve the level of reheat gas turbine combustion tuning technology.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.841-852
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• 2023

Gas turbines, which are used as generators for frequency regulation of the domestic power system, are increasing in use due to the carbon-neutral policy, quick startup and shutdown, and high thermal efficiency. Since the gas turbine rotates the turbine using high-temperature flame, the turbine inlet temperature is acting as a key factor determining the performance and lifespan of the device. However, since the inlet temperature cannot be directly measured, the temperature calculated by the manufacturer is used or the temperature predicted based on field experience is applied, which makes it difficult to operate and maintain the gas turbine in a stable manner. In this study, we present a model that can predict the inlet temperature of a reheat gas turbine based on Deep Neural Network (DNN), which is widely used in artificial neural networks, and verify the performance of the proposed DNN based on actual data.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2000.11a
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• pp.18-18
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• 2000

산업용 가스터빈에서 대기로 방출되는 배기열을 효과적으로 이용하기 위한 역 브레이튼 사이클 가스터빈(Reverse Brayton Cycle Gas Turbine) 엔진의 출력과 비연료 소비율 및 열효율을 기본 브레이튼 사이클 엔진, 재열사이클에 역 브레이튼 사이클을 추가한 엔진, 역 브레이튼 사이클에 중간냉각기를 추가한 엔진의 출력, 비연료 소비율 및 열효율을 비교하였다.(중략)

• The KSFM Journal of Fluid Machinery
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• v.9 no.6 s.39
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• pp.35-44
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• 2006

Operation conditions of a reheat cycle gas turbine for a combined cycle power plant was analyzed. Based on measured performance parameters of the gas turbine, a performance analysis program predicted component characteristic parameters such as compressor air flow, compressor efficiency, efficiencies of both the high and low pressure turbines, and coolant flows. The predicted air flow and its variation with the inlet guide vane setting were sufficiently accurate. The compressor running characteristic in terms of the relations between air flow, pressure ratio and efficiency was presented. The variations of the efficiencies of both the high and low pressure turbines were also presented. Almost constant flow functions of both turbines were predicted. The current methodology and obtained data can be utilized for performance diagnosis.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.30 no.5 s.248
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• pp.472-479
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• 2006

Recently, various methods have been developed to improve the performance of gas turbines for combined cycle power plants. This paper especially focused on the gas turbine with a reheat process. The purpose of this study is to analyze performance characteristics of a reheat-cycle gas turbine on both a design point and off-design operations. Results of the parametric study of this model show how operating and design parameters influence on the performance of the gas turbine. Moreover, possibilities for the analysis of off-design performance based on a self-generated compressor performance characteristic map are presented.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.34 no.1
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• pp.35-43
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• 2010

In this study, an ideal water-recirculated oxy-fuel power generation system is proposed. The results of parametric studies of the performance characteristics of the system are discussed. For a given choice of the turbine inlet temperature, the turbine, which produces power, can be either a gas or a steam turbine. For maximum efficiency, the turbine inlet temperature is selected as the level of state-of-the-art gas turbines and the reheat cycle may be adopted not only to enhance the turbine power but also to maintain dryness of the water with a turbine exhaust temperature that is as high as possible. To obtain a low condensation temperature for a high purity of $CO_2$, a relatively low pressure expansion process may be added. Finally, the performance of the water-recirculated oxy-fuel power generation system is discussed with reference to various operating parameters and system configurations. The optimal operating conditions for high performance and a high purity of $CO_2$ are proposed.