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.

Application of Methodologies to Evaluate the Health State of Gas Turbines in a Cogenerative Combined Cycle Power Plant

2002 ◽  
Author(s):  
M. Pinelli ◽  
M. Venturini
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Field Measurements ◽  
Combined Cycle ◽  
Health State ◽  
Combined Cycle Power Plant ◽  
Measurement Validation ◽  
Machine Health ◽  
Analysis Of Performance

In the paper, a comprehensive methodology for gas turbine health state determination is applied to a single-shaft Fiat Avio TG 20 gas turbine working in the cogenerative combined cycle power plant of Fiat – Mirafiori (Italy). In order to determine operating state variations from new and clean condition, the following procedures were applied to historical field measurements: • normalization procedure to determine the variations between measured and expected values; • inverse cycle technique to calculate the values of the characteristic parameters that are indices of the machine health state. The application of these techniques to long period operating data allowed measurement validation and the determination of the machine health state. The results showed the good capability of the developed techniques for the determination and the analysis of performance drop due to compressor fouling and to turbine malfunction.

Operating State Historical Data Analysis to Support Gas Turbine Malfunction Detection

2001 ◽  
Author(s):  
M. Pinelli ◽  
M. Venturini
Keyword(s):  
Gas Turbine ◽  
Field Measurements ◽  
Health State ◽  
Gas Compression ◽  
State Determination ◽  
First Results ◽  
Historical Data Analysis ◽  
On Line ◽  
Compressor Performance ◽  
Machine Health

Abstract The paper describes a methodology to determine gas turbine operating state based on the analysis of normalized field data. This methodology consists in normalizing measured value with respect to that expected, calculated in the actual boundary conditions and working point. The normalization procedure, if applied on line, provides useful information to support the machine Health State determination. In this paper, the methodology has been applied to field measurements taken on a 5 MW gas turbine running in a natural gas compression plant. The first results of field measurements analysis along a two year period are presented. Relations between compressor performance drops and the probable causes of malfunctioning have been identified. Some significant results are then presented.

A Multiple Operating Point Minimization Technique for Gas Turbine Operating State Determination

2002 ◽  
Author(s):  
M. Pinelli ◽  
P. R. Spina ◽  
M. Venturini
Keyword(s):  
Gas Turbine ◽  
Field Application ◽  
Operating Point ◽  
Health State ◽  
Characteristic Parameters ◽  
Estimation Errors ◽  
State Determination ◽  
Minimization Technique ◽  
Machine Health ◽  
Operating Points

Gas turbine operating state determination can be performed using Gas Path Analysis (GPA) techniques, which use measurements taken on the machine to calculate the characteristic parameters that are indices of the machine health state. The number and type of characteristic parameters that can be evaluated depend on the number and type of the available measured variables. Thus, when there are not enough measured variables to determine all the characteristic parameters, some of them have to be estimated independently of the actual gas turbine health state. In this way, variations due to aging or deterioration which, in the actual machine, may occur on these last characteristic parameters, cause estimation errors on the characteristic parameters assumed as problem unknowns. In the field application of GPA techniques the available instrumentation is often inadequate to ensure reliable operating state analysis. This problem may be partially overcome using a multiple operating point minimization technique. This consists of the determination of the characteristic parameters that minimize the sum of the square differences between measured and computed values of the measurable variables in multiple operating points. In this way the lack of data is overcome by data obtained in different operating points. This paper describes a procedure for gas turbine operating state determination based on a multiple operating point minimization technique and presents a study aimed at selecting the best set and number of operating points that have to be used.

A System for Health State Determination of Natural Gas Compression Gas Turbines

2001 ◽  
Author(s):  
R. Bettocchi ◽  
M. Pinelli ◽  
P. R. Spina ◽  
M. Venturini ◽  
S. Sebastianelli
Keyword(s):  
Natural Gas ◽  
Path Analysis ◽  
Gas Turbines ◽  
Diagnostic System ◽  
Health State ◽  
Compression System ◽  
Gas Compression ◽  
State Determination ◽  
System Maintenance

This paper illustrates the policy and objectives in compression system maintenance and describes a system for the health state determination of natural gas compression gas turbines based on “Gas Path Analysis”. Some results of the application of the diagnostic system to gas turbines working in a natural gas compression plant are presented.

Improvement of the Accuracy in Gas Turbine Health Determination

2001 ◽  
Author(s):  
M. Pinelli ◽  
M. Venturini
Keyword(s):  
Gas Turbine ◽  
Health Monitoring ◽  
Field Measurements ◽  
Monitoring Systems ◽  
Health State ◽  
State Determination ◽  
Health Monitoring Systems ◽  
Health Parameters

Health Monitoring Systems (HMS) based on operating state determination techniques that make use of field measurements are subjected to inaccuracies arising from measurements unreliability due to various kinds of uncertainties (such as sensors faults, measurements inaccuracies, etc.). In this paper, some techniques to improve the accuracy of gas turbine health state determination are presented: - a measurement conditioning technique based on the expected and trend values of measurements; - the evaluation of the best measurements/health parameters combination that should be used with respect to the gas turbine operating state determination.

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.

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.

scholarly journals Axial compressor fouling detection for gas turbine driven gas compression unit

2019 ◽  
Vol 15 (3) ◽  
pp. 1257
Author(s):  
Nurlan Batayev
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Power Plants ◽  
Detection System ◽  
Axial Compressor ◽  
Compressor Blades ◽  
Exhaust Temperature ◽  
Gas Compression ◽  
Compressor Power ◽  
Industrial Gas Turbine

<span>One of the main reasons of the performance degradation of gas turbines is the axial compressor fouling due to air pollutants. Considering the fact that the fouling leads to high consumption of fuel, reducing of the axial compressor’s discharge air pressure and increasing of the exhaust temperature, thus designing a compressor degradation detection system will allow prevent such issues. Many gas turbine plants lose power due to dirty axial compressor blades, which can add up to 4% loss of power. In case of power plants, the power loosing could be observed by less megawatts produced by generator. But in case of gas compression stations the effect of power loosing could not be quickly detected, because there is not direct measurement of the discharge power produced by gas turbine. This article represents technique for detection of gas turbine axial compressor degradation in case of gas turbine driven natural gas compression units. Calculation of the centrifugal gas compressor power performed using proven methodology. Approach for evaluation of the gas turbine performance based on machine learning prediction model is shown.  Adequacy of the model has been made to three weeks’ operation data of the 10 Megawatt class industrial gas turbine.</span>

scholarly journals Binary Cycle Gas Fired Turbine for Gas Compression Service

1962 ◽  
Author(s):  
Adrian W. McAnneny
Keyword(s):  
Gas Turbine ◽  
Centrifugal Compressor ◽  
Gas Turbines ◽  
High Speed ◽  
Waste Heat ◽  
Low Cost ◽  
Main Line ◽  
Gas Compression ◽  
Binary Cycle ◽  
Additional Equipment

Three years ago a survey was made of the various prime movers available to the pipeline industry for gas compression. This survey included gas turbines and two and four-cycle reciprocating gas engines. The purpose of this study was to determine which of the existing equipments would be most economical and whether or not there was a need for the development of additional equipment. As a result of this economic study, it appeared there was a definite requirement in the industry for a high-speed, low-cost, gas turbine-centrifugal compressor unit for both field and main-line-station gas compression. As a result of the studies two gas-turbine-driven centrifugal compressor units were placed in operation early in 1960 at Cypress Station near Houston, waste-heat recovery systems being installed in the summer of 1961. Performance tests were satisfactory and subsequently six small gas-engine-driven compressor units have been installed at two main-line compressor stations.

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