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.

Artificial Intelligence for the Diagnostics of Gas Turbines: Part I — Neural Network Approach

2005 ◽  
Author(s):  
R. Bettocchi ◽  
M. Pinelli ◽  
P. R. Spina ◽  
M. Venturini
Keyword(s):  
Neural Network ◽  
Measurement Uncertainty ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Measurement Errors ◽  
Learning Algorithm ◽  
Computational Time ◽  
Training Phase ◽  
Neural Network Approach ◽  
Single Output

In the paper, Neural Network (NN) models for gas turbine diagnostics are studied and developed. The analyses carried out are aimed at the selection of the most appropriate NN structure for gas turbine diagnostics, in terms of computational time of the NN training phase, accuracy and robustness with respect to measurement uncertainty. In particular, feed-forward NNs with a single hidden layer trained by using a back-propagation learning algorithm are considered and tested. Moreover, Multi-Input/Multi-Output NN architectures (i.e. NNs calculating all the system outputs) are compared to Multi-Input/Single-Output NNs, each of them calculating a single output of the system. The results obtained show that NNs are robust with respect to measurement uncertainty, if a sufficient number of training patterns are used. Moreover, Multi-Input/Multi-Output NNs trained with data corrupted with measurement errors seem to be the best compromise between the computational time required for NN training phase and the NN accuracy in performing gas turbine diagnostics.

Artificial Intelligence for the Diagnostics of Gas Turbines—Part I: Neural Network Approach

2006 ◽  
Vol 129 (3) ◽  
pp. 711-719 ◽  
Author(s):  
R. Bettocchi ◽  
M. Pinelli ◽  
P. R. Spina ◽  
M. Venturini
Keyword(s):  
Neural Network ◽  
Measurement Uncertainty ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Measurement Errors ◽  
Learning Algorithm ◽  
Computational Time ◽  
Training Phase ◽  
Neural Network Approach ◽  
Single Output

In the paper, neural network (NN) models for gas turbine diagnostics are studied and developed. The analyses carried out are aimed at the selection of the most appropriate NN structure for gas turbine diagnostics, in terms of computational time of the NN training phase, accuracy, and robustness with respect to measurement uncertainty. In particular, feed-forward NNs with a single hidden layer trained by using a back-propagation learning algorithm are considered and tested. Moreover, multi-input/multioutput NN architectures (i.e., NNs calculating all the system outputs) are compared to multi-input/single-output NNs, each of them calculating a single output of the system. The results obtained show that NNs are sufficiently robust with respect to measurement uncertainty, if a sufficient number of training patterns are used. Moreover, multi-input/multioutput NNs trained with data corrupted with measurement errors seem to be the best compromise between the computational time required for NN training phase and the NN accuracy in performing gas turbine diagnostics.

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.

Speech Noise Cancellation Based on a Neuro-Fuzzy System: Further Improvements

2001 ◽  
Vol 5 (1) ◽  
pp. 15-21
Author(s):  
Anna Esposito ◽  
◽  
Eugene C. Ezin ◽  
Carlos A. Reyes-Garcia ◽  
◽  
...  
Keyword(s):  
Nonlinear Distortion ◽  
Fuzzy Inference ◽  
Experimental System ◽  
Computational Time ◽  
Further Training ◽  
Inference System ◽  
Car Traffic ◽  
Neuro Fuzzy ◽  
Average Improvement ◽  
Set Up

This work reports on an experimental system based upon the Adaptive Neuro-Fuzzy Inference System (ANFIS) architecture, which is employed for identifying a nonlinear model of the unknown dynamic characteristics of the noise transmission paths. The output of this model is used to subtract the noisy components from the received signal. The novelty of the system described in the present paper, with respect to our previous work, consists in a different set up, which requires more fuzzy rules, generated by seven trapezoidal membership functions, and uses a second order it sinc function to generate the nonlinear distortion of the noise. Once trained for few epochs (only three) with a long sentence corrupted with babble noise, the FIS obtained, has the ability to clean speech sentences corrupted by babble and also by car, traffic, and white noise, in a computational time almost close to realtime. The average improvement, in terms of SNR, was 37 dB without further training.

Using Fuzzy Logic to Control Combined Cycle Gas Turbine During Ambient Computing Environment

2020 ◽  
Vol 11 (3) ◽  
pp. 106-130 ◽  
Author(s):  
Mostafa A. Elhosseini
Keyword(s):  
Fuzzy Logic ◽  
Gas Turbine ◽  
Fuzzy Inference ◽  
Combined Cycle ◽  
Operating Conditions ◽  
Superior Performance ◽  
Rule Base ◽  
Computing Environment ◽  
Inference System ◽  
Ambient Computing

The main aim of this article is to analyse and control a combined cycle gas turbine (CCGT) under normal and perturbation loading using a Fuzzy Logic Control (FLC) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) through an ambient computing environment. The main characteristics of ambient computing is invisible, embedded, easy to use, and adaptive to name a few. The current article proposes the employment of FLC and to control the operation of CCGT considering the system inputs uncertainty. The target of the FLC is to maintain the system speed, exhaust temperature, and airflow within the desired interval. ANFIS helps to get the optimal control parameter and construct the proper rule base with an appropriate membership function with reasonable accuracy. The simulation results demonstrate the ANFIS controller's superior performance over FLC as well as the traditional controller for normal operating conditions and load perturbation.

New 2-DOF PID Controller Tuning by Adaptive Neural Fuzzy Inference System for Gas Turbine Control System

2000 ◽  
Vol 4 (5) ◽  
pp. 341-348 ◽  
Author(s):  
Dong Hwa Kim ◽  
◽  
Chang Kee Jung ◽  
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Pid Controller ◽  
Waste Heat ◽  
Combined Cycle ◽  
Main Role ◽  
Controller Tuning ◽  
Inference System ◽  
Operating Data ◽  
Pid Controller Tuning

The purpose of introducing a combined cycle with gas turbines in power plants is to reduce loss of energy. Their main role lies in the utilization of waste heat that may be found in exhaust gases from the gas turbine or at some other points of the process to produce additional electricity. The efficiency of the plant exceeds 50%, while the traditional steam turbine plants is approximately 35% ∼ 40% or so. To date, the PID controller has been used to operate under such systems, but since PID controller gain manually has to be tuned by trial and error procedures, getting optimal PID gains is very difficult manually without control design experience. We studied acquiring transfer function from operating data on the Gun-san gas turbine in Korea and a new 2-DOF PID controller tuning by ANFIS is designed for the optimum control of the Guns-san gas turbine’s variables. Since the shape of a membership function in the ANFIS vary on the characteristics of plant, ANFIS-based control is effective for plants whose variables vary. Its results are compared to the conventional 2-DOF PID controller and represents satisfactory response. We expect this method will be used for another process because it is studied using actual operating data.

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.

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