Dynamic modeling of an industrial gas turbine in loading and unloading conditions using a gray box method

Mehrpanahi, Abdollah; Payganeh, Gholamhassan; Arbabtafti, Mohammadreza

doi:10.1016/j.energy.2016.12.012

Cited by 31 publications

(20 citation statements)

References 21 publications

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“…The mentioned turbine is a three-shaft type with a power of 26 MWs and approximate efficiency of 35%. 61,62 As illustrated in Figure 1, their structure consists of the three sections of the LP shaft, HP shaft and power turbine. Due to their rather complex structure, generation of accurate performance and dynamic models based on their details are highly complex tasks, with a time-consuming process for establishing compatibility between them.…”

Section: System Structure and Methodsmentioning

confidence: 99%

“…System identification signal has been generated according to the two inputs of fuel and ambient temperature, and finally, it has been attempted to obtain input signals compatible with the behavior of the validated dynamic model. 61,62 Dynamic modeling and system identification. For generating the outputs from input signals in this type of turbine, different scenarios can be used (Figures 4 to 6).…”

Section: Input Signalsmentioning

confidence: 99%

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Linear Transfer Functions Extraction for a Power Generation Three-Shaft Gas Turbine Based on Actual Specifications

Mehrpanahi

Payganeh

Arbabtafti

2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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The design and testing of various types of controllers require accurate and reliable transfer functions that are compatible with the performance condition of the system. In this paper, the linear and the nonlinear methods have been utilized for extracting the transfer functions for a three-shaft industrial gas turbine. The main variables are the fuel input and environment temperature, while the main outputs are the LPT exhaust gas temperature and the generated power. According to the nonlinear structure of the system and the input variation intervals, the quasi-amplitude-modulated pseudo random binary sequence signals have been utilized for generating input parameters. Finally, the results extracted from the selected methods were compared to the outputs of real performance data under loading and unloading conditions. Based on the comparison between the accuracies obtained by the results, the auto-regressive moving average with eXogenous, try and error transfer function and linearized Hammerstein model methods are proposed, respectively. The outcome of using the controller indicated higher compatibility from transfer functions as compared to the reference dynamic model.

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Section: System Structure and Methodsmentioning

confidence: 99%

Section: Input Signalsmentioning

confidence: 99%

Linear Transfer Functions Extraction for a Power Generation Three-Shaft Gas Turbine Based on Actual Specifications

Mehrpanahi

Payganeh

Arbabtafti

2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…This aspect is emphasized in several research works developed in the past including those (i) highlighting the benefits of dynamic simulation as a cost-efficient tool for improving the flexibility of dispatchable power generation in transient operation [16], and (ii) seeking to enhance the equipment behavior simulation and to improve operators training practices [17]. Other key related works include those dealing with the dynamic modeling of gas turbines based on grey [18] and black [19] box models, condition monitoring data [20], constant mass flow and inter-component volume methods [21], and physical phenomena associated with combustion processes and heat exchanging prevailing mechanisms [22].…”

Section: Introductionmentioning

confidence: 99%

On dynamic modeling of industrial gas turbines based on low calorific value (LCV) gaseous fuels

Celis

Pinto

Souza

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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Global energy demand is expected to increase in the following two decades. Operational flexibility of power generation systems is a key aspect when assessing potential solutions seeking to help meeting this expected increase in global energy demand. In low calorific value (LCV) gaseous fuels-fired power plants, it is paramount to consider the effects of the employed LCV/lean-gases-based fuels on the associated gas turbine systems and surrounding equipment. Moreover, because of the industrial processes usually occurring upstream these power plants, the fuel supply conditions change significantly during their normal operation. Gas turbines based on LCV gaseous fuels thus present a quite distinct engine behavior, and their modeling is therefore challenging. The dynamic modeling of such engines is the main focus of this work. Accordingly, the gas turbine dynamic model developed here is initially discussed, along with topics, such as gas turbine mass flow rates, cooling systems effectiveness and gas turbine component characteristics, relevant to the dynamic modeling of LCV fuels-based power plants. The use of the developed model for the simulation of an actual combined cycle power plant with cogeneration based on a LCV fuel is next highlighted. The main results show that overall acceptable agreements between computed parameters and actual power plant operating data are obtained. The corresponding average discrepancies range from 1 to 6%. In spite of the large number of factors directly influencing the numerical results obtained from the real-time simulations carried out, the power plant operating data trends are in general well reproduced by the computed results. The obtained results highlight in particular both the model applicability to operating scenarios presenting significant gradients in gas turbine characteristic parameters, and the need of including in the modeling key processes present in power plants actual operation. Keywords Power plants • Gas turbines • Low calorific value gaseous fuels • Dynamic modeling List of symbols Variables A Area (m 2) a Characteristic parameter, constant (−) b Characteristic parameter, constant (−) Cp Specific heat at constant pressure (kJ/kg K) c Characteristic parameter, constant (−) h Specific enthalpy (kJ/kg) ℏ Heat transfer (film) coefficient (kW/m 2 K) I Inertia moment (kg m 2) k Characteristic parameter, constant (−) M Mass (kg) m Mass flow rate (kg/s) N Rotational speed (rad/s) N * Non-dimensional speed (−) PR Pressure ratio (−) p Pressure (kPa) Q Heat rate (kW) T Temperature (K)

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“…The intermittent nature of renewables prompts the gas turbines to operate with increased flexibility, for supporting their renewable plant partners and maintaining the stability of the electricity grid [1,2]. Fast start up, shut down, load following modes, and part load operation [3,4,5] are dominating the operating regime of today's gas turbines. Understanding the behavior of these engines, under such demanding operating conditions, is crucial for their successful operation and maintenance (O&M).…”

Section: Introductionmentioning

confidence: 99%

Dynamic performance simulation and control of gas turbines used for hybrid gas/wind energy applications

Tsoutsanis

Meskin

2019

Applied Thermal Engineering

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Dynamic modeling of an industrial gas turbine in loading and unloading conditions using a gray box method

Cited by 31 publications

References 21 publications

Linear Transfer Functions Extraction for a Power Generation Three-Shaft Gas Turbine Based on Actual Specifications

Linear Transfer Functions Extraction for a Power Generation Three-Shaft Gas Turbine Based on Actual Specifications

On dynamic modeling of industrial gas turbines based on low calorific value (LCV) gaseous fuels

Dynamic performance simulation and control of gas turbines used for hybrid gas/wind energy applications

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