Improved Method for Gas-Turbine Off-Design Performance Adaptation Based on Field Data

2020 ◽  
Vol 142 (4) ◽  
Author(s):  
Shiyao Li ◽  
Zhenlin Li ◽  
Shuying Li
Keyword(s):  
Gas Turbine ◽  
Error Control ◽  
Field Data ◽  
Operating Conditions ◽  
Performance Parameters ◽  
Improved Method ◽  
Design Performance ◽  
Average Prediction Error ◽  
Comparison Results ◽  
Performance Adaptation

Abstract Obtaining accurate components' characteristic maps has great significant for gas-turbine operating optimization and gas-path fault diagnosis. A common approach is to modify the original components' characteristic maps by introducing correction factors, which is known as performance adaptation. Among the existing methods, total average prediction error of measurable parameters (MPTAPE) at specified conditions is used to evaluate the adaptation accuracy. However, when a gas turbine undergoes a field operation, the performance parameters of each component are zonally distributed under the operating conditions. Under such circumstances, randomly selecting a few data points as the error control points (ECPs) for performance adaptation may lead to an inappropriate correction of the characteristic maps, further lowering the prediction accuracy of the simulation model. In this paper, a genetic-algorithm-based improved performance adaptation method is proposed, which provides improvements in two aspects. In one aspect, similarity between the components' predicted performance curves and the performance regression curves is used as the criterion with which to evaluate the adaptation accuracy. In the other aspect, in the process of off-design performance adaptation, the performance parameters at the design point are recalibrated. The improved method has been verified by using rig test data and applied to field data of a GE LM2500+SAC gas turbine. The comparison results show that the improved method can obtain more accurate and stable adaptation results, while the computational load can be significantly reduced.

Nonlinear Multiple Points Gas Turbine Off-Design Performance Adaptation Using a Genetic Algorithm

2011 ◽  
Vol 133 (7) ◽  
Author(s):  
Y. G. Li ◽  
M. F. Abdul Ghafir ◽  
L. Wang ◽  
R. Singh ◽  
K. Huang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Scaling Factor ◽  
Performance Model ◽  
Operating Conditions ◽  
Performance Models ◽  
Design Performance ◽  
Turbine Performance ◽  
Thermodynamic Performance ◽  
Performance Adaptation

Accurate gas turbine performance models are crucial in many gas turbine performance analysis and gas path diagnostic applications. With current thermodynamic performance modeling techniques, the accuracy of gas turbine performance models at off-design conditions is determined by engine component characteristic maps obtained in rig tests and these maps may not be available to gas turbine users or may not be accurate for individual engines. In this paper, a nonlinear multiple point performance adaptation approach using a genetic algorithm is introduced with the aim to improve the performance prediction accuracy of gas turbine engines at different off-design conditions by calibrating the engine performance models against available test data. Such calibration is carried out with introduced nonlinear map scaling factor functions by “modifying” initially implemented component characteristic maps in the gas turbine thermodynamic performance models. A genetic algorithm is used to search for an optimal set of nonlinear scaling factor functions for the maps via an objective function that measures the difference between the simulated and actual gas path measurements. The developed off-design performance adaptation approach has been applied to a model single spool turbo-shaft aero gas turbine engine and has demonstrated a significant improvement in the performance model accuracy at off-design operating conditions.

Simulation of an Advanced Twin-Spool Industrial Gas Turbine

1992 ◽  
Vol 114 (2) ◽  
pp. 180-185 ◽  
Author(s):  
Ping Zhu ◽  
H. I. H. Saravanamuttoo
Keyword(s):  
Mathematical Model ◽  
Gas Turbine ◽  
Excellent Agreement ◽  
Field Data ◽  
Full Range ◽  
Operating Conditions ◽  
New Method ◽  
Operating Point ◽  
Design Performance ◽  
Industrial Gas Turbine

A full-range mathematical model of the LM-1600 gas turbine has been developed, for future use in EHM studies. No data were available from the manufacturer other than sales brochures giving some design and off-design performance. The model was developed using generalized component characteristics and shows excellent agreement with field data from a pipeline operator. A new method has been developed for doing the matching calculations, starting from the turbine (hot) end rather than from the compressor operating point. This method permits solution on a PC, and can be used for studying the full range of operating conditions and the development of fault matrices.

scholarly journals Simulation of an Advanced Twin-Spool Industrial Gas Turbine

1991 ◽  
Author(s):  
Ping Zhu ◽  
H. I. H. Saravanamuttoo
Keyword(s):  
Mathematical Model ◽  
Gas Turbine ◽  
Excellent Agreement ◽  
Field Data ◽  
Full Range ◽  
Operating Conditions ◽  
New Method ◽  
Operating Point ◽  
Design Performance ◽  
Industrial Gas Turbine

A full range mathematical model of the LM-1600 gas turbine has been developed, for future use in EHM studies. No data was available from the manufacturer other than sales brochures giving some design and off-design performance. The model was developed using generalized component characteristics and shows excellent agreement with field data from a pipeline operator. A new method has been developed for doing the matching calculations, starting from the turbine (hot) end rather than from the compressor operating point. This method permits solution on a PC, and can be used for studying the full range of operating conditions and the development of fault matrices.

Non-Linear Multiple Points Gas Turbine Off-Design Performance Adaptation Using a Genetic Algorithm

2010 ◽  
Author(s):  
Y. G. Li ◽  
M. F. Abdul Ghafir ◽  
L. Wang ◽  
R. Singh ◽  
K. Huang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Scaling Factor ◽  
Operating Conditions ◽  
Performance Models ◽  
Design Performance ◽  
Turbine Performance ◽  
Thermodynamic Performance ◽  
Non Linear ◽  
Performance Adaptation

Accurate gas turbine performance models are crucial in many gas turbine performance analysis and gas path diagnostic applications. With current thermodynamic performance modelling techniques, the accuracy of gas turbine performance models at off-design conditions is determined by engine component characteristic maps obtained in rig tests and these maps may not be available to gas turbine users or may not be accurate for individual engines. In this paper, a non-linear multiple point performance adaptation approach using a Genetic Algorithm is introduced with the aim to improve the performance prediction accuracy of gas turbine engines at different off-design conditions by calibrating the engine performance models against available test data. Such calibration is carried out with introduced non-linear map scaling factor functions by ‘modifying’ initially implemented component characteristic maps in the gas turbine thermodynamic performance models. A Genetic Algorithm is used to search for an optimal set of non-linear scaling factor functions for the maps via an objective function that measures the difference between the simulated and actual gas path measurements. The developed off-design performance adaptation approach has been applied to a model single spool turboshaft aero gas turbine engine and demonstrated a significant improvement in the performance model accuracy at off-design operating conditions.

scholarly journals A Comparative Study on Fault Detection Methods for Gas Turbine Combustion Systems

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 389
Author(s):  
Jinfu Liu ◽  
Zhenhua Long ◽  
Mingliang Bai ◽  
Linhai Zhu ◽  
Daren Yu
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Detection Methods ◽  
Distribution Model ◽  
Outlet Temperature ◽  
Combustion System ◽  
Comparison Results ◽  
Gas Turbine Combustion

As one of the core components of gas turbines, the combustion system operates in a high-temperature and high-pressure adverse environment, which makes it extremely prone to faults and catastrophic accidents. Therefore, it is necessary to monitor the combustion system to detect in a timely way whether its performance has deteriorated, to improve the safety and economy of gas turbine operation. However, the combustor outlet temperature is so high that conventional sensors cannot work in such a harsh environment for a long time. In practical application, temperature thermocouples distributed at the turbine outlet are used to monitor the exhaust gas temperature (EGT) to indirectly monitor the performance of the combustion system, but, the EGT is not only affected by faults but also influenced by many interference factors, such as ambient conditions, operating conditions, rotation and mixing of uneven hot gas, performance degradation of compressor, etc., which will reduce the sensitivity and reliability of fault detection. For this reason, many scholars have devoted themselves to the research of combustion system fault detection and proposed many excellent methods. However, few studies have compared these methods. This paper will introduce the main methods of combustion system fault detection and select current mainstream methods for analysis. And a circumferential temperature distribution model of gas turbine is established to simulate the EGT profile when a fault is coupled with interference factors, then use the simulation data to compare the detection results of selected methods. Besides, the comparison results are verified by the actual operation data of a gas turbine. Finally, through comparative research and mechanism analysis, the study points out a more suitable method for gas turbine combustion system fault detection and proposes possible development directions.

Analysis of Operational Effects on Cooled H-Class Gas Turbines

2019 ◽  
Author(s):  
Selcuk Can Uysal ◽  
James B. Black
Keyword(s):  
Gas Turbine ◽  
Turbine Blade ◽  
Power Output ◽  
Thermal Efficiency ◽  
Gas Turbines ◽  
Cooling System ◽  
Engine Performance ◽  
Heat Rate ◽  
Operating Conditions ◽  
Performance Parameters

Abstract During the operation of an industrial gas turbine, the engine deviates from its new condition performance because of several effects including dirt build-up, compressor fouling, material erosion, oxidation, corrosion, turbine blade burning or warping, thermal barrier coating (TBC) degradation, and turbine blade cooling channel clogging. Once these problems cause a significant impact on engine performance, maintenance actions are taken by the operators to restore the engine to new performance levels. It is important to quantify the impacts of these operational effects on the key engine performance parameters such as power output, heat rate and thermal efficiency for industrial gas turbines during the design phase. This information can be used to determine an engine maintenance schedule, which is directly related to maintenance costs during the anticipated operational time. A cooled gas turbine performance analysis model is used in this study to determine the impacts of the TBC degradation and compressor fouling on the engine performance by using three different H-Class gas turbine scenarios. The analytical tool that is used in this analysis is the Cooled Gas Turbine Model (CGTM) that was previously developed in MATLAB Simulink®. The CGTM evaluates the engine performance using operating conditions, polytropic efficiencies, material properties and cooling system information. To investigate the negative impacts on engine performance due to structural changes in TBC material, compressor fouling, and their combined effect, CGTM is used in this study for three different H-Class engine scenarios that have various compressor pressure ratios, turbine inlet temperatures, and power and thermal efficiency outputs; each determined to represent different classes of recent H-Class gas turbines. Experimental data on the changes in TBC performance are used as an input to the CGTM as a change in the TBC Biot number to observe the impacts on engine performance. The effect of compressor fouling is studied by changing the compressor discharge pressures and polytropic compressor efficiencies within the expected reduction ranges. The individual and combined effects of compressor fouling and TBC degradation are presented for the shaft power output, thermal efficiency and heat rate performance parameters. Possible improvements for the designers to reduce these impacts, and comparison of the reductions in engine performance parameters of the studied H-Class engine scenarios are also provided.

Gas Turbine Off-Design Performance Adaptation Using a Genetic Algorithm

2006 ◽  
Author(s):  
E. Lo Gatto ◽  
Y. G. Li ◽  
P. Pilidis
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Performance Prediction ◽  
Engine Performance ◽  
Design Data ◽  
Adaptive Approach ◽  
Design Point ◽  
Design Performance ◽  
Engine Design ◽  
Performance Adaptation

Gas turbine gas path diagnostics is heavily dependent on performance simulation models accurate enough around a chosen diagnostic operating point, such as design operating point. With current technology, gas turbine engine performance can be predicted easily with thermodynamic models and computer codes together with basic engine design data and empirical component information. However the accuracy of the prediction is highly dependent on the quality of those engine design data and empirical component information such as component characteristic maps but such expensive information is normally exclusive property of engine manufacturers and only partially disclosed to engine users. Alternatively, estimated design data and assumed component information are used in the performance prediction. Yet, such assumed component information may not be the same as those of real engines and therefore poor off-design performance prediction may be produced. This paper presents an adaptive method to improve the accuracy of off-design performance prediction of engine models near engine design point or other points where detailed knowledge is available. A novel definition of off-design scaling factors for the modification of compressor maps is developed. A Genetic Algorithm is used to search the best set of scaling factors in order to adapt the predicted off-design engine performance to observed engine off-design performance. As the outcome of the procedure, new compressor maps are produced and more accurate prediction of off-design performance is provided. The proposed off-design performance adaptation procedure is applied to a model civil aero engine to test the effectiveness of the adaptive approach. The results show that the developed adaptive approach, if properly applied, has great potential to improve the accuracy of engine off-design performance prediction in the vicinity of engine design point although it does not guarantee the prediction accuracy in the whole range of off-design conditions. Therefore, such adaptive approach provides an alternative method in producing good engine performance models for gas turbine gas path diagnostic analysis.

scholarly journals Diagnosis of Gas Turbine Operating Conditions by Means of the Inverse Cycle Calculation

1999 ◽  
Author(s):  
R. Bettocchi ◽  
P. R. Spina
Keyword(s):  
Objective Function ◽  
Gas Turbine ◽  
Operating Conditions ◽  
Performance Parameters ◽  
Characteristic Parameters ◽  
Flow Passage ◽  
Operating Condition ◽  
Expected Values ◽  
And Performance ◽  
Squared Residuals

This paper presents a method for the analysis of gas turbine operating state, which uses the Cycle-Deck developed by gas turbine manufacturers and the measurements taken by means of the standard machine instrumentation. The gas turbine operating condition analysis is performed “adapting” the characteristic geometric and performance parameters (i.e., characteristic flow passage areas and efficiencies of the compressor and turbine, combustor efficiency, etc …), used as inputs by the Cycle-Deck, until the computed estimates of the measurable parameters agree with the values measured on the gas turbine. This is done by minimizing an objective function built as the sum of the squared residuals between the computed and measured values of the same parameters. The analysis of the variations between computed and expected values of the characteristic parameters allows the localization of inefficient operations due to deterioration and faults.

Part-Load Performance of Gas Turbines: Part II — Multi-Point Adaptation With Compressor Map Generation and GA Optimization

2012 ◽  
Author(s):  
E. Tsoutsanis ◽  
Y. G. Li ◽  
P. Pilidis ◽  
M. Newby
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Combined Cycle ◽  
Performance Model ◽  
Part Load ◽  
Design Performance ◽  
Turbine Performance ◽  
Map Generation ◽  
Compressor Map ◽  
Performance Adaptation

Accurate gas turbine performance simulation is a vital aid to the operational and maintenance strategy of thermal plants having gas turbines as their prime mover. Prediction of the part load performance of a gas turbine depends on the quality of the engine’s component maps. Taking into consideration that compressor maps are proprietary information of the manufacturers, several methods have been developed to encounter the above limitation by scaling and adapting component maps. This part of the paper presents a new off-design performance adaptation approach with the use of a novel compressor map generation method and Genetic Algorithms (GA) optimization. A set of coefficients controlling a generic compressor performance map analytically is used in the optimization process for the adaptation of the gas turbine performance model to match available engine test data. The developed method has been tested with off-design performance simulations and applied to a GE LM2500+ aeroderivative gas turbine operating in Manx Electricity Authority’s combined cycle power plant in the Isle of Man. It has been also compared with an earlier off-design performance adaptation approach, and shown some advantages in the performance adaptation.

Compressor Performance With Water Injection

2001 ◽  
Author(s):  
J. H. Horlock
Keyword(s):  
Specific Heat ◽  
Gas Turbine ◽  
Dimensional Analysis ◽  
Water Injection ◽  
Operating Conditions ◽  
Water Droplets ◽  
One Dimensional ◽  
Design Performance ◽  
Effective Specific Heat ◽  
Compressor Performance

There has been renewed interest recently in the injection of water at inlet to gas turbine plants. As is to be expected there is a drop in temperature at the inlet face to the compressor and this obviously has an effect on compressor performance. But a second effect occurs within the early stages of the compressor itself, associated with an increase in the effective specific heat due to continuing evaporation of the water droplets. Consequently there are movements away from design operating conditions on the stage characteristics. A one-dimensional analysis of compressor off-design performance is developed to illustrate these effects, which appear to be appreciable, even for very small quantities of water injection.

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