Progress in Modeling and Control of Gas Turbine Power Generation Systems: A Survey

Mohamed, Omar; Khalil, Ashraf

doi:10.3390/en13092358

Cited by 36 publications

(20 citation statements)

References 77 publications

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“…The aspects of a multidisciplinary/interdisciplinary nature can also be deduced from the literature review that will be presented here; for more detailed literature, the reader may refer to the recent critical review written by the corresponding author [5]. The recently published dynamic models of GTs, whether combined with the steam cycle to become CCGT or as a single unit, can be established by physical laws, system identification, artificial intelligence, machine learning or deep learning techniques.…”

Section: Related Work and The Paper Contributionmentioning

confidence: 99%

“…Gas turbines' power share has increased progressively in the global power generation mix in later decades due to the progress in their design specifications, efficiency and reliability [1,2]. The field of system modeling and identification has facilitated the path towards many notable improvements, including higher cycle efficiencies and a reduced level of emissions; therefore, GT power generation technology has become an unavoidable choice for many developed and developing countries [3][4][5]. It can be more informative to provide an adequate motivation and background for this research before reviewing the literature/The operating principle of dual-fuel GT can be as simple as shown below in Figure 1.…”

Section: Introduction 1aims and Motivationsmentioning

confidence: 99%

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Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning

2022

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Accurate simulations of gas turbines’ dynamic performance are essential for improvements in their practical performance and advancements in sustainable energy production. This paper presents models with extremely accurate simulations for a real dual-fuel gas turbine using two state-of-the-art techniques of neural networks: the dynamic neural network and deep neural network. The dynamic neural network has been realized via a nonlinear autoregressive network with exogenous inputs (NARX) artificial neural network (ANN), and the deep neural network has been based on a convolutional neural network (CNN). The outputs selected for simulations are: the output power, the exhausted temperature and the turbine speed or system frequency, whereas the inputs are the natural gas (NG) control valve, the pilot gas control valve and the compressor variables. The data-sets have been prepared in three essential formats for the training and validation of the networks: normalized data, standardized data and SI units’ data. Rigorous effort has been carried out for wide-range trials regarding tweaking the network structures and hyper-parameters, which leads to highly satisfactory results for both models (overall, the minimum recorded MSE in the training of the MISO NARX was 6.2626 × 10−9 and the maximum MSE that was recorded for the MISO CNN was 2.9210 × 10−4, for more than 15 h of GT operation). The results have shown a comparable satisfactory performance for both dynamic NARX ANN and the CNN with a slight superiority of NARX. It can be newly argued that the dynamic ANN is better than the deep learning ANN for the time-based performance simulation of gas turbines (GTs).

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Section: Related Work and The Paper Contributionmentioning

confidence: 99%

Section: Introduction 1aims and Motivationsmentioning

confidence: 99%

Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning

2022

Self Cite

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“…Nowadays, many measures can be taken to improve the flexibility of power systems with high penetration of wind power. From the generation side, more gas-fired generators are built due to their high generation efficiency, fast ramp rate, and low CO2 emission intensity [4]. From the load side, the demand-side response can contribute to changing the hourly load profile to add the power system flexibility [5].…”

Section: Introductionmentioning

confidence: 99%

Flexible Dispatch for Integrated Power and Gas Systems Considering Power-to-Gas and Demand Response

et al. 2021

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Aiming at the problem of insufficient flexibility of the power system caused by large-scale wind power grid integration, a flexible economic dispatch model of the electricity-gas integrated system that considers power-to-gas and demand responses is proposed. First, it elaborates on the scheduling flexibility and demand response model. Secondly, the power system and the natural gas system are regarded as different stakeholders; with the goal of minimizing their respective operating costs, a two-tier distributed coordination optimization model of electricity and gas system is established. In order to achieve the coordination and optimization of the upper and lower systems, slack variables are introduced to describe the infeasible part of the power system scheduling results in the natural gas system and are used for interactive iterative solution of the model. The numerical results of the revised IEEE 30-node power system and 10-node natural gas system illustrate the effectiveness and necessity of the proposed model, as well as the superiority of comprehensively considering power-to-gas and demand response in improving the flexibility and economy of the power system.

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Section: Introductionmentioning

confidence: 99%

“…GTs have earned a privileged position among other electrical generation technologies due to its high efficiency and reliability, particularly when incorporated with combined cycle 4 . GTs are also known for their flexibility and regular availability 4 . However, the performance of the GT is influenced by both the efficiency of components and the turbine inlet temperature 5 , 6 .…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of a large-scale thermal power plant based on the best industrial practices

Najjar

Abu‐Shamleh

2020

Sci Rep

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The aim of this study is to assess and evaluate the performance of a large-scale thermal power plant (TPP). The performance rating was conducted in compliance with the statistical principles. The data for this analysis were obtained for a TPP with an installed capacity of 375 MW during a span of 8 years (2010–2017). Four parameters were used to evaluate the performance of the TPP including the availability, the reliability, the capacity factor, and the thermal efficiency. These parameters were calculated using a set of equations and then compared to the international best practices and target values. The results indicate that approximately 91% of the expected capacity was available throughout the studied period against the industry best practice of 95%. However, the average TPP’s reliability was found to be approximately 95% against the target value of 99.9%. Furthermore, the average capacity factor throughout the studied period is 70% as against the international value of 40–80%. Moreover, the thermal efficiency of the TPP is 40% against the target value of 49%. Due to the outage hours and malfunctions, the power losses throughout the studied period reached 846 MW. Overall, the analysis indicates that the studied TPP is not within the scope of the best industrial practices.

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Progress in Modeling and Control of Gas Turbine Power Generation Systems: A Survey

Cited by 36 publications

References 77 publications

Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning

Modeling a Practical Dual-Fuel Gas Turbine Power Generation System Using Dynamic Neural Network and Deep Learning

Flexible Dispatch for Integrated Power and Gas Systems Considering Power-to-Gas and Demand Response

Performance evaluation of a large-scale thermal power plant based on the best industrial practices

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