Effects of Humidity Condensation on the Trend of Gas Turbine Performance Deterioration

2015 ◽  
Vol 137 (12) ◽  
Author(s):  
Houman Hanachi ◽  
Jie Liu ◽  
Avisekh Banerjee ◽  
Ying Chen
Keyword(s):  
Gas Turbine ◽  
Particle Filtering ◽  
Fuzzy Inference ◽  
Water Mist ◽  
Operating Conditions ◽  
Full Time ◽  
Condensation Rate ◽  
Inference System ◽  
Deterioration Rate ◽  
Performance Deterioration

Performance deterioration in gas turbine engines (GTEs) depends on various factors in the ambient and the operating conditions. For example, humidity condensation at the inlet duct of a GTE creates water mist, which affects the fouling phenomena in the compressor and varies the performance. In this paper, the effective factors on the short-term performance deterioration of a GTE are identified and studied. GTE performance level is quantified with two physics-based performance indicators, calculated from the recorded operating data from the control system of a GTE over a full time between overhaul (TBO) period. A regularized particle filtering (RPF) framework is developed for filtering the indicator signals, and an adaptive neuro-fuzzy inference system (ANFIS) is then trained with the filtered signals and the effective ambient and the operating conditions, i.e., the power, the air mass flow, and the humidity condensation rate. The trained ANFIS model is then run to simulate the GTE performance deterioration in different conditions for system identification. The extracted behavior of the system clearly shows the dependency of the trend of performance deterioration on the operating conditions, especially the humidity condensation rate. The developed technique and the results can be utilized for GTE performance prediction, as well as for suggesting the optimum humidity supply at the GTE intake to control the performance deterioration rate.

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.

scholarly journals Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
İsmail Kıyak ◽  
Gökhan Gökmen ◽  
Gökhan Koçyiğit
Keyword(s):  
Fuzzy Inference ◽  
Prediction Method ◽  
Operating Conditions ◽  
Lifetime Prediction ◽  
Statistical Characteristics ◽  
Ambient Conditions ◽  
Inference System ◽  
Initial Task ◽  
Neuro Fuzzy ◽  
A Cell

Predicting the lifetime of a LED lighting system is important for the implementation of design specifications and comparative analysis of the financial competition of various illuminating systems. Most lifetime information published by LED manufacturers and standardization organizations is limited to certain temperature and current values. However, as a result of different working and ambient conditions throughout the whole operating period, significant differences in lifetimes can be observed. In this article, an advanced method of lifetime prediction is proposed considering the initial task areas and the statistical characteristics of the study values obtained in the accelerated fragmentation test. This study proposes a new method to predict the lifetime of COB LED using an artificial intelligence approach and LM-80 data. Accordingly, a database with 6000 hours of LM-80 data was created using the Neuro-Fuzzy (ANFIS) algorithm, and a highly accurate lifetime prediction method was developed. This method reveals an approximate similarity of 99.8506% with the benchmark lifetime. The proposed methodology may provide a useful guideline to lifetime predictions of LED-related products which can also be adapted to different operating conditions in a shorter time compared to conventional methods. At the same time, this method can be used in the life prediction of nanosensors and can be produced with the 3D technique.

Recurrent ANFIS-Coordinated Controller Design for Multimachine Power System with FACTS Devices

2016 ◽  
Vol 26 (02) ◽  
pp. 1750034 ◽  
Author(s):  
J. Sangeetha ◽  
P. Renuga
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Controller Design ◽  
Test System ◽  
Operating Conditions ◽  
Superior Performance ◽  
Control Technique ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Oscillation Damping

This paper proposes the design of auxiliary-coordinated controller for static VAR compensator (SVC) and thyristor-controlled series capacitor (TCSC) devices by adaptive fuzzy optimized technique for oscillation damping in multimachine power systems. The performance of the coordinated control of SVC and TCSC devices based on feedforward adaptive neuro fuzzy inference system (F-ANFIS) is compared with that of the adaptive neuro fuzzy inference system (ANFIS) structure based on recurrent adaptive neuro fuzzy inference system (R-ANFIS) network architecture. The objective of the coordinated controller design is to tune the parameters of SVC and TCSC fuzzy lead lag compensator simultaneously to minimize the deviation of rotor angle and rotor speed of the generators. The performance of the system is enhanced by optimally tuning the membership functions of fuzzy lead lag controller parameter of the flexible AC transmission system (FACTS) by R-ANFIS controller. The training data for F-ANFIS and R-ANFIS are generated by conventional linear control technique under various operating conditions. The offline trained controller tunes the parameter of lead lag controller in online. The oscillation damping ability of the system is analyzed for three-machine test system by calculating the standard deviation and cost function. The superior performance of R-ANFIS controller is compared with various particle swarm optimization-based feedforward ANFIS controllers available in literature.

scholarly journals Simulation of Mist Film Cooling at Gas Turbine Operating Conditions

2006 ◽  
Author(s):  
Ting Wang ◽  
Xianchang Li
Keyword(s):  
Gas Turbine ◽  
Film Cooling ◽  
Water Mist ◽  
Operating Conditions ◽  
Round Hole ◽  
Adiabatic Wall ◽  
Cooling Enhancement ◽  
Temperature And Pressure ◽  
Uniform Droplets ◽  
Air Film

Air film cooling has been successfully used to cool gas turbine hot sections for the last half century. A promising technology is proposed to enhance air film cooling with water mist injection. Numerical simulations have shown that injecting a small amount of water droplets into the cooling air improves film-cooling performance significantly. However, previous studies were conducted at conditions of low Reynolds number, temperature, and pressure to allow comparisons with experimental data. As a continuous effort to develop a realistic mist film cooling scheme, this paper focuses on simulating mist film cooling under typical gas turbine operating conditions of high temperature and pressure. The mainstream flow is at 15 atm with a temperature of 1561K. Both 2-D and 3-D cases are considered with different hole geometries on a flat surface, including a 2-D slot, a simple round hole, a compound-angle hole, and fan-shaped holes. The results show that 10%–20% mist (based on the coolant mass flow rate) achieves 5%–10% cooling enhancement and provides an additional 30–68K adiabatic wall temperature reduction. Uniform droplets of 5 to 20 μm are used. The droplet trajectories indicate the droplets tend to move away from the wall, which results in a lower cooling enhancement than under low pressure and temperature conditions. The commercial software Fluent (v. 6.2.16) is adopted in this study, and the standard k-ε model with enhanced wall treatment is adopted as the turbulence model.

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.

Allocating the Causes of Performance Deterioration in Combined Cycle Gas Turbine Plants

2002 ◽  
Vol 124 (2) ◽  
pp. 256-262 ◽  
Author(s):  
K. Mathioudakis ◽  
A. Stamatis ◽  
E. Bonataki
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Combined Cycle ◽  
Operating Conditions ◽  
Test Cases ◽  
Performance Deterioration ◽  
Baseline Values ◽  
The Way

A method for defining which parts of a combined cycle gas turbine (CCGT) power plant are responsible for performance deviations is presented. When the overall performances deviate from their baseline values, application of the method allows the determination of the component(s) of the plant, responsible for this deviation. It is shown that simple differentiation approaches may lead to erroneous conclusions, because they do not reveal the nature of deviations for individual components. Contributions of individual components are then assessed by separating deviations due to permanent changes and deviations due to change of operating conditions. A generalized formulation is presented together with the way of implementing it. Test cases are given, to make clearer the ideas put forward in the proposed method.

scholarly journals Voltage Stability Enhancement for Microgrid Using an SVC

2020 ◽  
Vol 18 (4.2) ◽  
pp. 39
Author(s):  
Dinh-Nhon Truong ◽  
Mi Sa Nguyen Thi ◽  
Van-Tri Bui ◽  
Thanh-Liem Tran
Keyword(s):  
Voltage Stability ◽  
Fuzzy Inference ◽  
Operating Conditions ◽  
Small Scale ◽  
Solar Photovoltaic ◽  
Inference System ◽  
Damping Characteristics ◽  
Voltage Stability Enhancement ◽  
Simulation Results ◽  
Stability Enhancement

This paper presents comparative simulation results of a Microgrid (MG) system using a Static Var Compensator (SVC) for improving the voltage stability of the studied system. An Adaptive Neural Fuzzy Inference System (ANFIS) controller is designed based on the feedback signals to control the proposed SVC. For simplicity, the studied MG system can be modeled as an equivalent small scale wind turbine generator (WTG) combine with a Solar Photovoltaic (PV) and a Battery that connected to the common AC bus. A time-domain approach based on nonlinear model simulations is systematically performed. By observing the simulation results it can be concluded that the designed ANFIS controller for SVC can offer better damping characteristics of the studied MG system under severe operating conditions

scholarly journals Artificial Intelligence-Based Controller for DC-DC Flyback Converter

2019 ◽  
Vol 9 (23) ◽  
pp. 5108
Author(s):  
Muhammad Arslan Shahid ◽  
Ghulam Abbas ◽  
Mohammad Rashid Hussain ◽  
Muhammad Usman Asad ◽  
Umar Farooq ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Input Voltage ◽  
Operating Conditions ◽  
Model Parameters ◽  
Learning Capability ◽  
Inference System ◽  
Flyback Converter ◽  
Response Characteristics ◽  
Voltage Controller ◽  
Neuro Fuzzy

This paper presents an intelligent voltage controller designed on the basis of an adaptive neuro-fuzzy inference system (ANFIS) for a flyback converter (FC) working in continuous conduction mode (CCM). The union of fuzzy logic (FL) and adaptive neural networks (ANN) makes ANFIS more robust against model parameters’ uncertainties and perturbations in input voltage or load current. ANFIS inherits the advantages of structured knowledge representation from FL and learning capability from NN. Comparative analysis showed that the ANFIS controller offers not only the superior transient response characteristics, but also excellent steady-state characteristics compared to those of the FL controller (FLC) and proportional–integral–derivative (PID) controllers, thus validating its superiority over these traditional controllers. For this purpose, MATLAB/Simulink environment-based simulation results are presented for validation of the proposed converter compensated system under all operating conditions.

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