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.

Modern opportunities for preparation of ultra-pure water for feeding high-performance boiler plants

2020 ◽  
pp. 74-84
Author(s):  
A.A. Filimonova ◽  
◽  
N.D. Chichirova ◽  
A.A. Chichirov ◽  
A.A. Batalova ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
High Performance ◽  
Waste Heat ◽  
Pure Water ◽  
Combined Cycle ◽  
Feed Water ◽  
Operational Characteristics ◽  
Treatment Equipment

The article provides an overview of modern high-performance combined-cycle plants and gas turbine plants with waste heat boilers. The forecast for the introduction of gas turbine equipment at TPPs in the world and in Russia is presented. The classification of gas turbines according to the degree of energy efficiency and operational characteristics is given. Waste heat boilers are characterized in terms of design and associated performance and efficiency. To achieve high operating parameters of gas turbine and boiler equipment, it is necessary to use, among other things, modern water treatment equipment. The article discusses modern effective technologies, the leading place among which is occupied by membrane, and especially baromembrane methods of preparing feed water-waste heat boilers. At the same time, the ion exchange technology remains one of the most demanded at TPPs in the Russian Federation.

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.

Analysis of Long-Term Gas Turbine Operation With a Model-Based Data Reconciliation Technique

2015 ◽  
Author(s):  
Christian Rudolf ◽  
Manfred Wirsum ◽  
Martin Gassner ◽  
Stefano Bernero
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Engine Performance ◽  
Combined Cycle ◽  
Data Reconciliation ◽  
Data Set ◽  
Operating Data ◽  
Term Operation

The continuous monitoring of gas turbines in commercial power plant operation provides long-term engine data of field units. Evaluation of the engine performance is challenging as, apart from variations of operating points and environmental conditions, the state of the engine is subject to changes due to the ageing of engine components. The measurement devices applied to the unit influence the analysis by means of their accuracy, which may itself alter with time. Furthermore, the available measurements do usually not cover all necessary information for the evaluation of the engine performance. To overcome these issues, this paper describes a method to systematically evaluate long term operation data without the incorporation of engine design models since the latter do not cover performance changes when components are ageing. Key focus of the methodology thereby is to assess long-term emission performance in the most reliable manner. The analysis applies a data reconciliation method to long-term operating data in order to model the engine performance including non-measured variables and to account for measurement inaccuracies. This procedure relies on redundancies in the data set due to available measurements and the identification of suitable additional constituting equations that are independent of component ageing. The resulting over-determined set of equations allows for performing a data set optimization with respect to a minimal cumulated deviation to the measurement values, which represents the most probable, real state of the engine. The paper illustrates the development and application of the method to analyse the gas path of a commercial gas turbine in a combined cycle power plant with long-term operating data.

Modelling of Gas Turbine NOx Emissions Based on Long-Term Operation Data

2016 ◽  
Author(s):  
Christian Rudolf ◽  
Manfred Wirsum ◽  
Martin Gassner ◽  
Benjamin Timo Zoller ◽  
Stefano Bernero
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Engine Performance ◽  
Combined Cycle ◽  
Data Set ◽  
Operating Data ◽  
Emission Performance ◽  
Term Operation

The continuous monitoring of gas turbines in commercial power plant operation provides long-term engine data of field units. Evaluation of the engine performance from such data is challenging since, apart from variations of operating points and ambient conditions, the state of the engine is subject to change due to ageing of engine components. The installed measurement devices influence the analysis due to their accuracy, which may itself alter with time. Furthermore, the available measurements usually do not cover all necessary information for assessment of the engine performance. To overcome these issues, this paper describes a method to systematically evaluate long term operation data without the incorporation of engine design models that depict the design state of the engine, but do not cover performance changes when components are ageing. Key focus of the methodology is thereby to assess long-term emission performance in the most reliable manner. The analysis applies a data reconciliation method to long-term operating data in order to model the engine performance including non-measured variables and to account for measurement inaccuracies. This procedure relies on redundancies in the data set due to available measurements and the identification of suitable additional constituting equations that are independent of component ageing. The resulting over-determined set of equations allows for performing a data set optimization with respect to a minimal cumulated deviation to the measurement values, which represents the most probable, real state of the engine. The paper illustrates the development and application of the method for analysing emission performance with long-term operating data of a commercial gas turbine combined cycle power plant.

Increasing the Efficiency of a Gas Turbine Power Plant by Waste Heat Recovery

2009 ◽  
Author(s):  
P. Shukla ◽  
M. Izadi ◽  
P. Marzocca ◽  
D. K. Aidun
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Power Plants ◽  
Waste Heat ◽  
Current System ◽  
Temperature Drop ◽  
Combined Cycle ◽  
Gas Turbine Power Plant ◽  
Steam Cycle

The objective of this paper is to evaluate methods to increase the efficiency of a gas turbine power plant. Advanced intercooled gas turbine power plants are quite efficient, efficiency reaching about 47%. The efficiency could be further increased by recovering wasted heat. The system under consideration includes an intercooled gas turbine. The heat is being wasted in the intercooler and a temperature drop happens at the exhaust. For the current system it will be shown that combining the gas cycle with steam cycle and removing the intercooler will increase the efficiency of the combined cycle power plant up to 60%. In combined cycles the efficiency depends greatly on the exhaust temperature of the gas turbine and the higher gas temperature leads to the higher efficiency of the steam cycle. The analysis shows that the latest gas turbines with the intercooler can be employed more efficiently in a combined cycle power application if the intercooler is removed from the system.

PID CONTROLLER TUNING USING ANT COLONY METHOD FOR SERVO HYDRAULIC SYSTEM

2019 ◽  
Vol 42 (4) ◽  
pp. 148-152
Author(s):  
Shaymaa Mahmood Mahdi ◽  
Karam Samir Khalid ◽  
Shakir Mahmood Mahdi
Keyword(s):  
Hydraulic System ◽  
Pid Controller ◽  
Ant Colony ◽  
Controller Tuning ◽  
Pid Controller Tuning
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