Modeling and Simulation of the Transient Behavior of an Industrial Power Plant Gas Turbine

Asgari, Hamid; Venturini, Mauro; Chen, Xiaoqi; Sainudiin, Raazesh

doi:10.1115/1.4026215

Cited by 51 publications

(29 citation statements)

References 30 publications

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“…A Simulink model to predict the transient response of an industrial power plant gas turbine (IPGT) was reported by Asgari et al [29]. The Simulink Model was constructed from thermodynamic and energy equations.…”

Section: Introductionmentioning

confidence: 99%

Gas Path Fault and Degradation Modelling in Twin-Shaft Gas Turbines

2018

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In this study, an assessment of degradation and failure modes in the gas-path components of twin-shaft industrial gas turbines (IGTs) has been carried out through a model-based analysis. Measurements from twin-shaft IGTs operated in the field and denoting reduction in engine performance attributed to compressor fouling conditions, hot-end blade turbine damage, and failure in the variable stator guide vane (VSGV) mechanism of the compressor have been considered for the analysis. The measurements were compared with simulated data from a thermodynamic model constructed in a Simulink environment, which predicts the physical parameters (pressure and temperature) across the different stations of the IGT. The model predicts engine health parameters, e.g., component efficiencies and flow capacities, which are not available in the engine field data. The results show that it is possible to simulate the change in physical parameters across the IGT during degradation and failure in the components by varying component efficiencies and flow capacities during IGT simulation. The results also demonstrate that the model can predict the measured field data attributed to failure in the gas-path components of twin-shaft IGTs. The estimated health parameters during degradation or failure in the gas-path components can assist the development of health-index prognostic methods for operational engine performance prediction.

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Section: Introductionmentioning

confidence: 99%

Gas Path Fault and Degradation Modelling in Twin-Shaft Gas Turbines

2018

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“…A number of studies have applied various regression methods and other machine learning techniques to CCGT performance prediction [9] and fault detection [10]. The application of artificial neural networks (ANN) as reliable alternatives for various simulation control modelling and fault detection applications for CCGT is now well established [11][12][13]. Other machine learning methods including Least Squares Support Vector Machines (LSSVM) [14] and adaptive neuro-fuzzy inference system (ANFIS) [15] are also applied for modelling and controlling GT.…”

Section: Introductionmentioning

confidence: 99%

Combined cycle gas turbine power output prediction and data mining with optimized data matching algorithm

Wood

2020

SN Appl. Sci.

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Electrical power output (PE) for a combined cycle gas turbine (CCGT) consisting of 9568 data records collected over a 6-year period is evaluated by the transparent open box (TOB) machine-learning method to provide accurate PE predictions and insight to prediction errors. The PE predictions derived by applying the TOB optimized data matching technique are more accurate than published predictions for the dataset from fifteen correlation-based, machine-learning algorithms. TOB achieves this high-accuracy using a tuning subset of < 150 (~ 1.5%) data records. Its accuracy is confirmed by testing the optimized solutions against all dataset records in 15 runs spread across five shuffled datasets. The dataset has a few extreme outliers associated with its four independent variables and these negatively impact the prediction accuracy of machine-learning methods. Through its transparency and forensic-like auditability of its calculations for individual data records, the TOB algorithm is able to mine the dataset to provide useful insight to the interactions of the outliers with other data records. This enables a filtered dataset (9533 records), excluding 35 carefully selected data records, to be customized to deliver much improved prediction accuracy (RMSE = 2.89 MW). Mining the dataset also reveals significant differences in prediction accuracy achieved for different sectors of the PE distribution. This insight identifies that prediction accuracy could be further improved by dividing the dataset into separately optimized subsets, three along its main PE trend plus a fourth, small subset consisting of the outliers. The TOB algorithm demonstrates its value as a machine-learning tool capable of generating accurate predictions and easily auditable data mining. It is well suited for CCGT efficiency and performance optimization.

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“…They also defined a novel performance function for better analysis of quick transients and could design an accurate and efficient tool for simulations that could also be used for diagnosis of gas turbines. Asgari et al [8] performed a series of studies on the start-up phase of a heavy-duty industrial gas turbine using neural network approach implemented in MATLAB. Both NARX and Simulink models were capable of appropriate prediction but not the turbine behavior during the cold start-up phase.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Control of the Starter Motor and Start-Up Phase for Gas Turbines

2019

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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.

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Modeling and Simulation of the Transient Behavior of an Industrial Power Plant Gas Turbine

Cited by 51 publications

References 30 publications

Gas Path Fault and Degradation Modelling in Twin-Shaft Gas Turbines

Gas Path Fault and Degradation Modelling in Twin-Shaft Gas Turbines

Combined cycle gas turbine power output prediction and data mining with optimized data matching algorithm

Modeling and Control of the Starter Motor and Start-Up Phase for Gas Turbines

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