Support for Operation Strategy in CHP Plants With Gas Turbines

2006 ◽  
Author(s):  
Gerard Kosman ◽  
Tadeusz Chmielniak ◽  
Wojciech Kosman
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Extended Period ◽  
Health State ◽  
Ambient Conditions ◽  
Operation Conditions ◽  
Thermal Measurements ◽  
Time Of Operation ◽  
Diagnostic Module

This paper presents procedure, which supports planning a strategy of operation, repairs and modernizations. Reliability and effectiveness are assumed to form the criteria for appropriate operation with a special attention to working costs. The procedure involves diagnostic analysis. Information derived from diagnostic may be utilized in many ways. It allows to determine losses, which derive from components wear or improper operation, and track the wear rate of machines components. This in turn allows to assess the losses, which appear in case of extended period between routine repairs. The most important application of the diagnostic results is the determination of the working costs for a CHP plant. It establishes a relation between the working costs of a gas turbine and its future time of operation. In addition it analyses the influence of the parameters independent of the gas turbine user (such as ambient conditions) on the operation and costs. The calculations presented in this paper involve a diagnostic module designed for uncooled and cooled gas turbines. The health state is assessed through a set of performance indices. Thermal measurements are the input data for the module, which may utilize even a small number of available measurements. The working costs create the basis for the procedure, which supports planning the strategy of operation and repairs. The procedure consists of several diagnostic rules. It draws conclusions from given premises. A premise includes a set of data, which involve among others working costs calculated according to health state. A conclusion indicates whether a further operation is possible and under what circumstances. The circumstances specify any required adjustments of the operation conditions or suggest an exchange or repair of some turbine components, which might be damaged.

Numerical Assessment of SCAP: A Passive System for Preventing Thermoacoustic Oscillations in Gas Turbine Annular Combustors

2007 ◽  
Author(s):  
Stefano Tiribuzi
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Research Unit ◽  
Combined Cycle ◽  
Computational Time ◽  
Computational Domain ◽  
Ambient Conditions ◽  
Pressure Oscillations ◽  
Operation Conditions ◽  
Thermoacoustic Oscillations

ENEL operates a dozen combined cycle units whose V94.3A gas turbines are equipped with annular combustors. In such lean premixed gas turbines, particular operation conditions could trigger large pressure oscillations due to thermoacoustic instabilities. The ENEL Research unit is studying this phenomenon in order to find out methods which could avoid or mitigate such events. The use of effective numerical analysis techniques allowed us to investigate the realistic time evolution and behaviour of the acoustic fields associated with this phenomenon. KIEN, an in-house low diffusive URANS code capable of simulating 3D reactive flows, has been used in the Very Rough Grid approach. This approach permits the simulation, with a reasonable computational time, of quite long real transients with a computational domain extended over all the resonant volumes involved in the acoustic phenomenon. The V94.3A gas turbine model was set up with a full combustor 3D grid, going from the compressor outlet up to the turbine inlet, including both the annular plenum and the annular combustion chamber. The grid extends over the entire circular angle, including all the 24 premixed burners. Numerical runs were performed with the normal V94.3A combustor configuration, with input parameters set so as no oscillations develop in the standard ambient conditions. Wide pressure oscillations on the contrary are associated with the circumferential acoustic modes of the combustor, which have their onset and grow when winter ambient conditions are assumed. These results also confirmed that the sustaining mechanism is based on the equivalence ratio fluctuation of premix mixture and that plenum plays an important role in such mechanism. Based on these findings, a system for controlling the thermoacoustic oscillation has been conceived (Patent Pending), which acts on the plenum side of the combustor. This system, called SCAP (Segmentation of Combustor Annular Plenum), is based on the subdivision of the plenum annular volume by means of a few meridionally oriented walls. Repetition of KIEN runs with a SCAP configuration, in which a suitable number of segmentation walls were properly arranged in the annular plenum, demonstrated the effectiveness of this solution in preventing the development of wide thermoacoustic oscillations in the combustor.

scholarly journals Gas Turbine Health State Determination: Methodology Approach and Field Application

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Michele Pinelli ◽  
Pier Ruggero Spina ◽  
Mauro Venturini
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Field Measurements ◽  
Field Application ◽  
Diagnostic Process ◽  
Health State ◽  
Gas Compression ◽  
State Determination ◽  
Measurement Validation ◽  
Machine Health

A reduction of gas turbine maintenance costs, together with the increase in machine availability and the reduction of management costs, is usually expected when gas turbine preventive maintenance is performed in parallel to on-condition maintenance. However, on-condition maintenance requires up-to-date knowledge of the machine health state. The gas turbine health state can be determined by means of Gas Path Analysis (GPA) techniques, which allow the calculation of machine health state indices, starting from measurements taken on the machine. Since the GPA technique makes use of field measurements, the reliability of the diagnostic process also depends on measurement reliability. In this paper, a comprehensive approach for both the measurement validation and health state determination of gas turbines is discussed, and its application to a 5 MW gas turbine working in a natural gas compression plant is presented.

Determination of Cycle Configuration of Gas Turbines and Aircraft Engines by Optimization Procedure

1990 ◽  
Author(s):  
Yoshiharu Tsujikawa ◽  
Makoto Nagaoka
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Specific Impulse ◽  
Optimization Procedure ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Aircraft Engines ◽  
Scramjet Engine ◽  
Aero Engines

This paper is devoted to the analyses and optimization of simple and sophisticated cycles, particularly for various gas turbine engines and aero-engines (including scramjet engine) to achive the maximum performance. The optimization of such criteria as thermal efficiency, specific output and total performance for gas turbine engines, and overall efficiency, non-dimensional thrust and specific impulse for aero-engines have been performed by the optimization procedure with multiplier method. The comparisons of results with analytical solutions establishes the validity of the optimization procedure.

Operation Experiences of Siemens IGCC Gas Turbines Using Gasification Products From Coal and Refinery Residues

2000 ◽  
Author(s):  
M. Huth ◽  
A. Heilos ◽  
G. Gaio ◽  
J. Karg
Keyword(s):  
Power Generation ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Combined Cycle ◽  
Strong Impact ◽  
Coal Gas ◽  
Operation Conditions ◽  
Wide Range ◽  
Commercial Operation ◽  
Burner Design

The Integrated Gasification Combined Cycle concept is an emerging technology that enables an efficient and clean use of coal as well as residuals in power generation. After several years of development and demonstration operation, now the technology has reached the status for commercial operation. SIEMENS is engaged in 3 IGCC plants in Europe which are currently in operation. Each of these plants has specific characteristics leading to a wide range of experiences in development and operation of IGCC gas turbines fired with low to medium LHV syngases. The worlds first IGCC plant of commercial size at Buggenum/Netherlands (Demkolec) has already demonstrated that IGCC is a very efficient power generation technology for a great variety of coals and with a great potential for future commercial market penetration. The end of the demonstration period of the Buggenum IGCC plant and the start of its commercial operation has been dated on January 1, 1998. After optimisations during the demonstration period the gas turbine is running with good performance and high availability and has exceeded 18000 hours of operation on coal gas. The air-side fully integrated Buggenum plant, equipped with a Siemens V94.2 gas turbine, has been the first field test for the Siemens syngas combustion concept, which enables operation with very low NOx emission levels between 120–600 g/MWh NOx corresponding to 6–30 ppm(v) (15%O2) and less than 5 ppm(v) CO at baseload. During early commissioning the syngas nozzle has been recognised as the most important part with strong impact on combustion behaviour. Consequently the burner design has been adjusted to enable quick and easy changes of the important syngas nozzle. This design feature enables fast and efficient optimisations of the combustion performance and the possibility for easy adjustments to different syngases with a large variation in composition and LHV. During several test runs the gas turbine proved the required degree of flexibility and the capability to handle transient operation conditions during emergency cases. The fully air-side integrated IGCC plant at Puertollano/Spain (Elcogas), using the advanced Siemens V94.3 gas turbine (enhanced efficiency), is now running successfully on coal gas. The coal gas composition at this plant is similar to the Buggenum example. The emission performance is comparable to Buggenum with its very low emission levels. Currently the gas turbine is running for the requirements of final optimization runs of the gasifier unit. The third IGCC plant (ISAB) equipped with Siemens gas turbine technology is located at Priolo near Siracusa at Sicilly/Italy. Two Siemens V94.2K (modified compressor) gas turbines are part of this “air side non-integrated” IGCC plant. The feedstock of the gasification process is a refinery residue (asphalt). The LHV is almost twice compared to the Buggenum or Puertollano case. For operation with this gas, the coal gas burner design was adjusted and extensively tested. IGCC operation without air extraction has been made possible by modifying the compressor, giving enhanced surge margins. Commissioning on syngas for the first of the two gas turbines started in mid of August 1999 and was almost finished at the end of August 1999. The second machine followed at the end of October 1999. Since this both machines are released for operation on syngas up to baseload.

Early Fault Detection of Hot Components in Gas Turbines

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
Liu Jinfu ◽  
Liu Jiao ◽  
Wan Jie ◽  
Wang Zhongqi ◽  
Yu Daren
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Working Environment ◽  
Detection Sensitivity ◽  
Normal Operation ◽  
Large Temperature ◽  
Ambient Conditions ◽  
Fisher Discriminant Analysis ◽  
Early Fault Detection

The working environment of hot components is the most adverse of all gas turbine components. Malfunction of hot components is often followed by catastrophic consequences. Early fault detection plays a significant role in detecting performance deterioration immediately and reducing unscheduled maintenance. In this paper, an early fault detection method is introduced to detect early fault symptoms of hot components in gas turbines. The exhaust gas temperature (EGT) is usually used to monitor the performance of the hot components. The EGT is measured by several thermocouples distributed equally at the outlet of the gas turbine. EGT profile is symmetrical when the unit is in normal operation. And the faults of hot components lead to large temperature differences between different thermocouple readings. However, interferences can potentially affect temperature differences, and sometimes, especially in the early stages of the fault, its influence can be even higher than that of the faults. To improve the detection sensitivity, the influence of interferences must be eliminated. The two main interferences investigated in this study are associated with the operating and ambient conditions, and the structure deviation of different combustion chambers caused by processing and installation errors. Based on the basic principles of gas turbines and Fisher discriminant analysis (FDA), a new detection indicator is presented that characterizes the intrinsic structure information of the hot components. Using this new indicator, the interferences involving the certainty and the uncertainty are suppressed and the sensitivity of early fault detection in gas turbine hot components is improved. The robustness and the sensitivity of the proposed method are verified by actual data from a Taurus 70 gas turbine produced by Solar Turbines.

On-Line Performance Evaluation of CHP Plant With Cooled Gas Turbine Supported by Off-Line Analysis Results

2005 ◽  
Author(s):  
Tadeusz Chmielniak ◽  
Wojciech Kosman ◽  
Gerard Kosman
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Input Data ◽  
Health State ◽  
Diagnostic Analysis ◽  
Additional Input ◽  
On Line ◽  
Diagnostic Mode ◽  
Line Analysis ◽  
Diagnostic Investigations

This paper presents a methodology of diagnostic investigations for gas turbines. The key feature is that the analysis is carried out in two modes: off-line and on-line. The first mode is performed periodically. It involves detailed measurements. Values obtained from measurements create the input data for further analysis. Health state of a gas turbine is then evaluated. The evaluation bases on calculation of several health state parameters. The on-line diagnostic mode uses these parameters as a reference state. The usual lack of measurements available in the on-line investigations creates the need for additional input data for the analysis. Therefore diagnostic investigations are supported by the results from the off-line mode. One of the main problems to be solved in diagnostic analysis is the appropriate modeling of gas turbine operation. An approach presented here regards the operation in various conditions, meaning also off-design operation.

Artificial Intelligence for the Diagnostics of Gas Turbines—Part II: Neuro-Fuzzy Approach

2006 ◽  
Vol 129 (3) ◽  
pp. 720-729 ◽  
Author(s):  
R. Bettocchi ◽  
M. Pinelli ◽  
P. R. Spina ◽  
M. Venturini
Keyword(s):  
Measurement Uncertainty ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Fuzzy Inference ◽  
Combined Cycle ◽  
Computational Time ◽  
Health State ◽  
Training Phase ◽  
Inference System ◽  
Neuro Fuzzy

In the paper, neuro-fuzzy systems (NFSs) for gas turbine diagnostics are studied and developed. The same procedure used previously for the setup of neural network (NN) models (Bettocchi, R., Pinelli, M., Spina, P. R., and Venturini, M., 2007, ASME J. Eng. Gas Turbines Power, 129(3), pp. 711–719) was used. In particular, the same database of patterns was used for both training and testing the NFSs. This database was obtained by running a cycle program, calibrated on a 255MW single-shaft gas turbine working in the ENEL combined cycle power plant of La Spezia (Italy). The database contains the variations of the Health Indices (which are the characteristic parameters that are indices of gas turbine health state, such as efficiencies and characteristic flow passage areas of compressor and turbine) and the corresponding variations of the measured quantities with respect to the values in new and clean conditions. The analyses carried out are aimed at the selection of the most appropriate NFS structure for gas turbine diagnostics, in terms of computational time of the NFS training phase, accuracy, and robustness towards measurement uncertainty during simulations. In particular, adaptive neuro-fuzzy inference system (ANFIS) architectures were considered and tested, and their performance was compared to that obtainable by using the NN models. An analysis was also performed in order to identify the most significant ANFIS inputs. The results obtained show that ANFISs are robust with respect to measurement uncertainty, and, in all the cases analyzed, the performance (in terms of accuracy during simulations and time spent for the training phase) proved to be better than that obtainable by multi-input/multioutput (MIMO) and multi-input/single-output (MISO) neural networks trained and tested on the same data.

Determination of the optimum performance of gas turbines

2000 ◽  
Vol 214 (1) ◽  
pp. 243-255 ◽  
Author(s):  
J. H. Horlock ◽  
W. A. Woods
Keyword(s):  
Gas Turbine ◽  
Thermal Efficiency ◽  
Gas Turbines ◽  
Working Fluid ◽  
Optimum Conditions ◽  
Perfect Gas ◽  
Pressure Losses ◽  
Real Effects ◽  
Turbine Performance

Earlier analytical and graphical treatments of gas turbine performance, assuming the working fluid to be a perfect gas, are developed to allow for ‘non-perfect’ gas effects and pressure losses. The pressure ratios for maximum power and maximum thermal efficiency are determined analytically; the graphical presentations of performance based on the earlier approach are also modified. It is shown that the optimum conditions previously determined from the ‘air standard’ analyses may be changed quite substantially by the inclusion of the ‘real’ effects.

Artificial Intelligence for the Diagnostics of Gas Turbines: Part II — Neuro-Fuzzy Approach

2005 ◽  
Author(s):  
R. Bettocchi ◽  
M. Pinelli ◽  
P. R. Spina ◽  
M. Venturini
Keyword(s):  
Measurement Uncertainty ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Fuzzy Inference ◽  
Combined Cycle ◽  
Computational Time ◽  
Health State ◽  
Training Phase ◽  
Inference System ◽  
Neuro Fuzzy

In the paper, Neuro-Fuzzy Systems (NFSs) for gas turbine diagnostics are studied and developed. The same procedure used previously for the set up of Neural Network (NN) models was used. In particular, the same database of patterns was used for both training and testing the NFSs. This database was obtained by running a Cycle Program, calibrated on a 255 MW single shaft gas turbine working in the ENEL combined cycle power plant of La Spezia (Italy). The database contains the variations of the Health Indices (which are the characteristic parameters that are indices of gas turbine health state, such as efficiencies and characteristic flow passage areas of compressor and turbine) and the corresponding variations of the measured quantities with respect to the values in new and clean conditions. The analyses carried out are aimed at the selection of the most appropriate NFS structure for gas turbine diagnostics, in terms of computational time of the NFS training phase, accuracy and robustness towards measurement uncertainty during simulations. In particular, Adaptive Neuro-Fuzzy Inference System (ANFIS) architectures were considered and tested, and their performance was compared to that obtainable by using the NN models. An analysis was also performed in order to identify the most significant ANFIS inputs. The results obtained show that ANFISs are robust with respect to measurement uncertainty, and, in all the cases analyzed, the performance (in terms of accuracy during simulations and time spent for the training phase) proved to be better than that obtainable by MIMO and MISO Neural Networks trained and tested on the same data.

Heavy Duty Gas Turbine Simulation: Global Performances Estimation and Secondary Air System Modifications

2006 ◽  
Author(s):  
Carlo Carcasci ◽  
Bruno Facchini ◽  
Stefano Gori ◽  
Luca Bozzi ◽  
Stefano Traverso
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Cooling System ◽  
Energy System ◽  
Operating Conditions ◽  
Coupled Analysis ◽  
Ambient Conditions ◽  
Air System ◽  
Secondary Air ◽  
And Performance

This paper reviews a modular-structured program ESMS (Energy System Modular Simulation) for the simulation of air-cooled gas turbines cycles, including the calculation of the secondary air system. The program has been tested for the Ansaldo Energia gas turbine V94.3A, which is one of the more advanced models in the family Vx4.3A with a rated power of 270 MW. V94.3A cooling system has been modeled with SASAC (Secondary Air System Ansaldo Code), the Ansaldo code used to predict the structure of the flow through the internal air system. The objective of the work was to investigate the tuning of the analytical program on the basis of the data from design and performance codes in use at Ansaldo Energy Gas Turbine Department. The results, both at base load over different ambient conditions and in critical off-design operating points (full-speed-no-load and minimum-load), have been compared with APC (Ansaldo Performance Code) and confirmed by field data. The coupled analysis of cycle and cooling network shows interesting evaluations for components life estimation and reliability during off-design operating conditions.

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