Modelling and Analysis of Gas Turbine Performance Deterioration

1992 ◽  
Author(s):  
A. N. Lakshminarasimha ◽  
M. P. Boyce ◽  
C. B. Meher-Homji
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Field Data ◽  
Field Observations ◽  
Turbine Performance ◽  
Common Causes ◽  
Performance Deterioration ◽  
Simulation Results ◽  
Compressor Map

The effects of performance deterioration in both land and aircraft gas turbines are presented in this paper. Models for two of the most common causes of deterioration viz. fouling and deterioration are presented. A stage stacking procedure which uses new installed engine field data for compressor map development is described. The results of the effect of fouling in a powerplant gas turbine and that of erosion in a aircraft gas turbine are presented. Also described are methods of fault threshold quantification and fault matrix simulation. Results of the analyses were found to be consistent with field observations.

Modeling and Analysis of Gas Turbine Performance Deterioration

1994 ◽  
Vol 116 (1) ◽  
pp. 46-52 ◽  
Author(s):  
A. N. Lakshminarasimha ◽  
M. P. Boyce ◽  
C. B. Meher-Homji
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Field Data ◽  
Field Observations ◽  
Modeling And Analysis ◽  
Turbine Performance ◽  
Common Causes ◽  
Performance Deterioration ◽  
Simulation Results ◽  
Compressor Map

The effects of performance deterioration in both land and aircraft gas turbines are presented in this paper. Models for two of the most common causes of deterioration, viz., fouling and erosion, are presented. A stage-stacking procedure, which uses new installed engine field data for compressor map development, is described. The results of the effect of fouling in a powerplant gas turbine and that of erosion in a aircraft gas turbine are presented. Also described are methods of fault threshold quantification and fault matrix simulation. Results of the analyses were found to be consistent with field observations.

Prediction Reliability of a Statistical Methodology for Gas Turbine Prognostics

2012 ◽  
Vol 134 (10) ◽  
Author(s):  
Mauro Venturini ◽  
Nicola Puggina
Keyword(s):  
Gas Turbine ◽  
Correction Factor ◽  
Gas Turbines ◽  
Threshold Value ◽  
False Alarms ◽  
Prediction Errors ◽  
Past Behavior ◽  
Turbine Performance ◽  
Performance Deterioration ◽  
Prediction Reliability

The performance of gas turbines degrades over time and, as a consequence, a decrease in gas turbine performance parameters also occurs, so that they may fall below a given threshold value. Therefore, corrective maintenance actions are required to bring the system back to an acceptable operating condition. In today’s competitive market, the prognosis of the time evolution of system performance is also recommended, in such a manner as to take appropriate action before any serious malfunctioning has occurred and, as a consequence, to improve system reliability and availability. Successful prognostics should be as accurate as possible, because false alarms cause unnecessary maintenance and nonprofitable stops. For these reasons, a prognostic methodology, developed by the authors, is applied in this paper to assess its prediction reliability for several degradation scenarios typical of gas turbine performance deterioration. The methodology makes use of the Monte Carlo statistical method to provide, on the basis of the recordings of past behavior, a prediction of future availability, i.e., the probability that the considered machine or component can be found in the operational state at a given time in the future. The analyses carried out in this paper aim to assess the influence of the degradation scenario on methodology prediction reliability, as a function of a user-defined threshold and minimum value allowed for the parameter under consideration. A technique is also presented and discussed, in order to improve methodology prediction reliability by means a correction factor applied to the time points used for methodology calibration. The results presented in this paper show that, for all the considered degradation scenarios, the prediction error is lower than 4% (in most cases, it is even lower than 2%), if the availability is estimated for the next trend, while it is not higher than 12%, if the availability is estimated five trends ahead. The application of a proper correction factor allows the prediction errors after five trends to be reduced to approximately 5%.

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.

Part-Load Performance of Gas Turbines: Part I — A Novel Compressor Map Generation Approach Suitable for Adaptive Simulation

2012 ◽  
Author(s):  
E. Tsoutsanis ◽  
Y. G. Li ◽  
P. Pilidis ◽  
M. Newby
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Primary Objective ◽  
Operating Conditions ◽  
Small Range ◽  
Part Load ◽  
Turbine Performance ◽  
Wide Range ◽  
Map Generation ◽  
Compressor Map

Part-load performance prediction of gas turbines is strongly dependent on detailed understanding of engine component behavior and mainly that of compressors. The accuracy of gas turbine engine models relies on the compressor performance maps, which are obtained in costly rig tests and remain manufacturer’s proprietary information. The gas turbine research community has addressed this limitation by scaling default generic compressor maps in order to match the targeted off-design measurements. This approach is efficient in small range of operating conditions but becomes less accurate for wide range of operating conditions. In this part of the paper a novel method of compressor map generation which has a primary objective to improve the accuracy of engine models performance at part load conditions is presented. This is to generate a generic form of equations to represent the lines of constant speed and constant efficiency of the compressor map for a generic compressor. The parameters that control the shape of the compressor map have been expressed in their simplest form in order to aid the adaptation process. The proposed compressor map generation method has the capacity to refine current gas turbine performance adaptation techniques, and it has been integrated into Cranfield’s PYTHIA gas turbine performance simulation and diagnostics software tool.

Prediction Reliability of a Statistical Methodology for Gas Turbine Prognostics

2012 ◽  
Author(s):  
Mauro Venturini ◽  
Nicola Puggina
Keyword(s):  
Gas Turbine ◽  
Correction Factor ◽  
Gas Turbines ◽  
Threshold Value ◽  
False Alarms ◽  
Prediction Errors ◽  
Past Behavior ◽  
Turbine Performance ◽  
Performance Deterioration ◽  
Prediction Reliability

The performance of gas turbines degrades over time and, as a consequence, a decrease in gas turbine performance parameters also occurs, so that they may fall below a given threshold value. Therefore, corrective maintenance actions are required to bring the system back to an acceptable operating condition. In today’s competitive market, the prognosis of the time evolution of system performance is also recommended, in such a manner as to take appropriate action before any serious malfunctioning has occurred and, as a consequence, to improve system reliability and availability. Successful prognostics should be as accurate as possible, because false alarms cause unnecessary maintenance and non-profitable stops. For these reasons, a prognostic methodology, developed by the authors, is applied in this paper to assess its prediction reliability for several degradation scenarios typical of gas turbine performance deterioration. The methodology makes use of the Monte Carlo statistical method to provide, on the basis of the recordings of past behavior, a prediction of future availability, i.e. the probability that the considered machine or component can be found in the operational state at a given time in the future. The analyses carried out in this paper aim to assess the influence of the degradation scenario on methodology prediction reliability, as a function of a user-defined threshold and minimum value allowed for the parameter under consideration. A technique is also presented and discussed, in order to improve methodology prediction reliability by means a correction factor applied to the time points used for methodology calibration. The results presented in this paper show that, for all the considered degradation scenarios, the prediction error is lower than 4% (in most cases, it is even lower than 2%), if the availability is estimated for the next trend, while it is not higher than 12%, if the availability is estimated five trends ahead. The application of a proper correction factor allows the prediction errors after five trends to be reduced to approximately 5%.

An Efficient Component Map Generation Method for Prediction of Gas Turbine Performance

2014 ◽  
Author(s):  
Elias Tsoutsanis ◽  
Nader Meskin ◽  
Mohieddine Benammar ◽  
Khashayar Khorasani
Keyword(s):  
Gas Turbine ◽  
Performance Prediction ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Small Range ◽  
Engine Model ◽  
Turbine Performance ◽  
Map Generation ◽  
Compressor Map ◽  
Component Map

Improving efficiency, reliability and availability of gas turbines have become more than ever one of the main areas of interest in gas turbine research. This is mainly due to the stringent environmental regulations that have to be met in such a mature technology sector; and consequently new research challenges have been identified. One of these involves the establishment of high fidelity, accurate, and computationally efficient engine performance simulation, diagnosis and prognosis technology. Performance prediction of gas turbines is strongly dependent on detailed understanding of the engine component behaviour. Compressors are of special interest because they can generate all sorts of operability problems like surge, stall and flutter; and their operating line is determined by the turbine characteristic. Compressor performance maps, which are obtained in costly rig tests and remain manufacturers proprietary information, impose a stringent limitation that has been commonly resolved by scaling default generic maps in order to match the targeted off-design or engine degraded measurements. This approach is efficient in small range of operating conditions but becomes less accurate for a wider range of operations. In this paper, a novel compressor map generation method, with the primary objective of improving the accuracy and fidelity of the engine model performance prediction is developed and presented. A new compressor map fitting and modelling method is introduced to simultaneously determine the best elliptical curves to a set of compressor map data. The coefficients that determine the shape of compressor maps’ curves have been analyzed and tuned through a multi-objective optimization algorithm in order to meet the targeted set of measurements. The proposed component map generation method is developed in the object oriented Matlab/Simulink environment and is integrated in a dynamic gas turbine engine model. The accuracy of this method is evaluated for off-design steady state and transient engine conditions. The proposed compressor map generation method has the capability to refine current gas turbine performance prediction approaches and to improve model-based diagnostic techniques.

scholarly journals Non-linear model calibration for off-design performance prediction of gas turbines with experimental data

2017 ◽  
Vol 121 (1245) ◽  
pp. 1758-1777 ◽  
Author(s):  
Elias Tsoutsanis ◽  
Yi-Guang Li ◽  
Pericles Pilidis ◽  
Mike Newby
Keyword(s):  
Experimental Data ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Simulation Software ◽  
Design Performance ◽  
Turbine Performance ◽  
Map Generation ◽  
Compressor Map ◽  
Performance Adaptation ◽  
Improved Accuracy

ABSTRACTOne of the key challenges of the gas turbine community is to empower the condition based maintenance with simulation, diagnostic and prognostic tools which improve the reliability and availability of the engines. Within this context, the inverse adaptive modelling methods have generated much attention for their capability to tune engine models for matching experimental test data and/or simulation data. In this study, an integrated performance adaptation system for estimating the steady-state off-design performance of gas turbines is presented. In the system, a novel method for compressor map generation and a genetic algorithm-based method for engine off-design performance adaptation are introduced. The methods are integrated into PYTHIA gas turbine simulation software, developed at Cranfield University and tested with experimental data of an aero derivative gas turbine. The results demonstrate the promising capabilities of the proposed system for accurate prediction of the gas turbine performance. This is achieved by matching simultaneously a set of multiple off-design operating points. It is proven that the proposed methods and the system have the capability to progressively update and refine gas turbine performance models with improved accuracy, which is crucial for model-based gas path diagnostics and prognostics.

Evolution of Gas Turbine Intake Air Filtration in a 420 MW Cogeneration Plant

2014 ◽  
Author(s):  
Steve Ingistov ◽  
Michael Milos ◽  
Rakesh K. Bhargava
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Economic Benefits ◽  
Cogeneration Plant ◽  
Air Filtration ◽  
Air Filter ◽  
Filter System ◽  
Turbine Performance ◽  
Filtration System ◽  
Operational Data

A suitable inlet air filter system is required for a gas turbine, depending on installation site and its environmental conditions, to minimize contaminants entering the compressor section in order to maintain gas turbine performance. This paper describes evolution of inlet air filter systems utilized at the 420 MW Watson Cogeneration Plant consisting of four GE 7EA gas turbines since commissioning of the plant in November 1987. Changes to the inlet air filtration system became necessary due to system limitations, a desire to reduce operational and maintenance costs, and enhance overall plant performance. Based on approximately 2 years of operational data with the latest filtration system combined with other operational experiences of more than 25 years, it is shown that implementation of the high efficiency particulate air filter system provides reduced number of crank washes, gas turbine performance improvement and significant economic benefits compared to the traditional synthetic media type filters. Reasons for improved gas turbine performance and associated economic benefits, observed via actual operational data, with use of the latest filter system are discussed in this paper.

Influence of Blade Deterioration on Compressor and Turbine Performance

2008 ◽  
Author(s):  
M. Morini ◽  
M. Pinelli ◽  
P. R. Spina ◽  
M. Venturini
Keyword(s):  
Gas Turbine ◽  
Mechanical Damage ◽  
Turbine Stage ◽  
Geometric Data ◽  
Turbine Performance ◽  
State Determination ◽  
Performance Curves ◽  
Common Causes ◽  
Simple Scaling ◽  
Main Effects

Gas turbine operating state determination consists of the assessment of the modification, due to deterioration and fault, of performance and geometric data characterizing machine components. One of the main effects of deterioration and fault is the modification of compressor and turbine performance maps. Since detailed information about actual modification of component maps is usually unavailable, many authors simulate the effects of deterioration and fault by a simple scaling of the map itself. In this paper, stage-by-stage models of the compressor and the turbine are used in order to assess the actual modification of compressor and turbine performance maps due to blade deterioration. The compressor is modeled by using generalized performance curves of each stage matched by means of a stage-stacking procedure. Each turbine stage is instead modeled as a couple of nozzles, a fixed one (stator) and a moving one (rotor). The results obtained by simulating some of the most common causes of blade deterioration (i.e., compressor fouling, compressor mechanical damage, turbine fouling and turbine erosion, occurring in one or more stages simultaneously) are reported in this paper. Moreover, compressor and turbine maps obtained through a stage-by-stage procedure are compared to the ones obtained by means of map scaling.

Marine Gas Turbine Performance Model for More Electric Ships

2011 ◽  
Author(s):  
George M. Koutsothanasis ◽  
Anestis I. Kalfas ◽  
Georgios Doulgeris
Keyword(s):  
Gas Turbine ◽  
Fuel Consumption ◽  
Diesel Engines ◽  
Gas Turbines ◽  
Electric Propulsion ◽  
Operating Conditions ◽  
Prime Mover ◽  
Creep Life ◽  
Hull Fouling ◽  
Turbine Performance

This paper presents the benefits of the more electric vessels powered by hybrid engines and investigates the suitability of a particular prime-mover for a specific ship type using a simulation environment which can approach the actual operating conditions. The performance of a mega yacht (70m), powered by two 4.5MW recuperated gas turbines is examined in different voyage scenarios. The analysis is accomplished for a variety of weather and hull fouling conditions using a marine gas turbine performance software which is constituted by six modules based on analytical methods. In the present study, the marine simulation model is used to predict the fuel consumption and emission levels for various conditions of sea state, ambient and sea temperatures and hull fouling profiles. In addition, using the aforementioned parameters, the variation of engine and propeller efficiency can be estimated. Finally, the software is coupled to a creep life prediction tool, able to calculate the consumption of creep life of the high pressure turbine blading for the predefined missions. The results of the performance analysis show that a mega yacht powered by gas turbines can have comparable fuel consumption with the same vessel powered by high speed Diesel engines in the range of 10MW. In such Integrated Full Electric Propulsion (IFEP) environment the gas turbine provides a comprehensive candidate as a prime mover, mainly due to its compactness being highly valued in such application and its eco-friendly operation. The simulation of different voyage cases shows that cleaning the hull of the vessel, the fuel consumption reduces up to 16%. The benefit of the clean hull becomes even greater when adverse weather condition is considered. Additionally, the specific mega yacht when powered by two 4.2MW Diesel engines has a cruising speed of 15 knots with an average fuel consumption of 10.5 [tonne/day]. The same ship powered by two 4.5MW gas turbines has a cruising speed of 22 knots which means that a journey can be completed 31.8% faster, which reduces impressively the total steaming time. However the gas turbine powered yacht consumes 9 [tonne/day] more fuel. Considering the above, Gas Turbine looks to be the only solution which fulfills the next generation sophisticated high powered ship engine requirements.

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