Online Adaptive Control Tuning in a Gas Turbine Hybrid System

2020 ◽  
Author(s):  
Harry Bonilla-Alvarado ◽  
Bernardo Restrepo ◽  
Paolo Pezzini ◽  
Lawrence Shadle ◽  
David Tucker ◽  
...  
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Mass Flow ◽  
Pid Controller ◽  
Standard Procedure ◽  
Full Range ◽  
Test Procedure ◽  
Operating Conditions ◽  
Control Technique ◽  
Pid Controllers

Abstract Proportional integral and derivative (PID) controllers are the most popular technique used in the power plant industry for process automation. However, the performance of these controllers may be affected due to variations in the power plant operating conditions, such as between startup, shutdown, and baseload/part-load operation. To maintain the desired performance over the full range of operations, PID controllers are always retuned in most power plants. During this retuning process, the operator takes control of the manipulated variable to perform a standard procedure based on a bump test. This procedure is generally performed to characterize the relationship between the manipulated variable and the process variable at each operating condition. After the bump test, the operator generally applies basic guidelines to assign new parameters to the PID controller. In this paper, the Model Reference Adaptive Controller (MRAC) control technique was implemented to update the PID controller parameters online without performing the bump test procedure. This approach allows updating the controller response on-the-fly while the power plant is running and without using the standard procedure based on a bump test. The MRAC was developed and demonstrated in the gas turbine hybrid cycle at the National Energy Technology Laboratory (NETL) to retune a critically damped mass flow PID controller into an over-damped response. Results showed stable performance during mass flow setpoint steps and also a stable update of the controller parameters.

Coal-Derived Gas Utilization in Combined Gas-Steam Cycle Power Plants

1990 ◽  
Author(s):  
R. Tuccillo ◽  
G. Fontana ◽  
E. Jannelli
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Mass Flow ◽  
Power Plants ◽  
Coal Gasification ◽  
Saturation Pressure ◽  
Combined Cycle ◽  
Operating Conditions ◽  
Specific Work ◽  
Steam Cycle

In this paper, a general analysis of combined gas-steam cycles for power plants firing with both hydrocarbons and coal derived gas is reported. The purpose of this paper is to study the influence on power plants performance of different kind of fuels and to evaluate the most significant parameters of both gas and combined cycle. Results are presented for plant overall efficiency and net specific work, steam to gas mass flow ratio, dimensionless gas turbine specific speed and diameter, CO2 emissions etc., as functions of gas cycle pressure ratio and of the combustion temperature. Furthermore, for an existing power plant with a 120 MW gas turbine, the authors try to establish in which measure the combined cycle characteristic parameters, the gas turbine operating conditions, and the heat recovery steam generator efficiency, are modified by using synthetic fuels of different composition and calorific value. The influence is also analyzed either of bottoming steam cycle saturation pressure or — in a dual pressure steam cycle — of dimensionless fraction of steam mass flow in high pressure stream. The acquired results seem to constitute useful information on the criteria for the optimal design of a new integrated coal gasification combined cycle (IGCC) power plant.

Optimal tuning of PID controller in time delay system: a review on various optimization techniques

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Thomas George ◽  
V. Ganesan
Keyword(s):  
Time Delay ◽  
Pid Control ◽  
Pid Controller ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Delay Systems ◽  
Pid Controllers ◽  
Time Delay Systems ◽  
Process Industries ◽  
First Order

AbstractThe processes which contain at least one pole at the origin are known as integrating systems. The process output varies continuously with time at certain speed when they are disturbed from the equilibrium operating point by any environment disturbance/change in input conditions and thus they are considered as non-self-regulating. In most occasions this phenomenon is very disadvantageous and dangerous. Therefore it is always a challenging task to efficient control such kind of processes. Depending upon the number of poles present at the origin and also on the location of other poles in transfer function different types of integrating systems exist. Stable first order plus time delay systems with an integrator (FOPTDI), unstable first order plus time delay systems with an integrator (UFOPTDI), pure integrating plus time delay (PIPTD) systems and double integrating plus time delay (DIPTD) systems are the classifications of integrating systems. By using a well-controlled positioning stage the advances in micro and nano metrology are inevitable in order satisfy the need to maintain the product quality of miniaturized components. As proportional-integral-derivative (PID) controllers are very simple to tune, easy to understand and robust in control they are widely implemented in many of the chemical process industries. In industries this PID control is the most common control algorithm used and also this has been universally accepted in industrial control. In a wide range of operating conditions the popularity of PID controllers can be attributed partly to their robust performance and partly to their functional simplicity which allows engineers to operate them in a simple, straight forward manner. One of the accepted control algorithms by the process industries is the PID control. However, in order to accomplish high precision positioning performance and to build a robust controller tuning of the key parameters in a PID controller is most inevitable. Therefore, for PID controllers many tuning methods are proposed. the main factors that lead to lifetime reduction in gain loss of PID parameters are described in This paper and also the main methods used for gain tuning based on optimization approach analysis is reviewed. The advantages and disadvantages of each one are outlined and some future directions for research are analyzed.

Combustion Tuning for a Gas Turbine Power Plant Using Data-Driven and Machine Learning Approach

2021 ◽  
Vol 143 (3) ◽  
Author(s):  
Suhui Li ◽  
Huaxin Zhu ◽  
Min Zhu ◽  
Gang Zhao ◽  
Xiaofeng Wei
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Gas Turbine ◽  
Operating Conditions ◽  
Data Driven ◽  
Ann Model ◽  
Promising Alternative ◽  
Combustion Performance ◽  
Wide Range ◽  
Gas Turbine Power Plant

Abstract Conventional physics-based or experimental-based approaches for gas turbine combustion tuning are time consuming and cost intensive. Recent advances in data analytics provide an alternative method. In this paper, we present a cross-disciplinary study on the combustion tuning of an F-class gas turbine that combines machine learning with physics understanding. An artificial-neural-network-based (ANN) model is developed to predict the combustion performance (outputs), including NOx emissions, combustion dynamics, combustor vibrational acceleration, and turbine exhaust temperature. The inputs of the ANN model are identified by analyzing the key operating variables that impact the combustion performance, such as the pilot and the premixed fuel flow, and the inlet guide vane angle. The ANN model is trained by field data from an F-class gas turbine power plant. The trained model is able to describe the combustion performance at an acceptable accuracy in a wide range of operating conditions. In combination with the genetic algorithm, the model is applied to optimize the combustion performance of the gas turbine. Results demonstrate that the data-driven method offers a promising alternative for combustion tuning at a low cost and fast turn-around.

Multivariable PID Controller for Unidentified Plant

1982 ◽  
Vol 104 (3) ◽  
pp. 270-274 ◽  
Author(s):  
S. Thompson
Keyword(s):  
Mathematical Model ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Pid Controller ◽  
Nuclear Power ◽  
Distillation Column ◽  
Open Loop ◽  
Pid Controllers ◽  
Simulation Studies ◽  
Plant Simulation

A procedure is presented for designing multivariable controllers for unidentified plant. It is assumed that the open-loop plant is stable and its response to step inputs are basically nonoscillatory. For such plant, no mathematical model is required in order to generate multivariable I, PI, or PID controllers. Method of tuning the controllers are also presented and demonstrated, first on a low order linear distillation column model, and finally on a high order, nonlinear, once-through boiler model typical of the type used in nuclear power plant simulation studies.

Influence of Steam Injection on Thermal Efficiency and Operating Temperature of GE-F5 Gas Turbines Applying VODOLEY System

2005 ◽  
Author(s):  
Mohsen Ghazikhani ◽  
Nima Manshoori ◽  
Davood Tafazoli
Keyword(s):  
Power Plant ◽  
Ambient Temperature ◽  
Gas Turbine ◽  
Mass Flow Rate ◽  
Mass Flow ◽  
Flow Rate ◽  
Thermal Efficiency ◽  
Gas Turbines ◽  
Steam Injection ◽  
Gas Turbine Cycle

An industrial gas turbine has the characteristic that turbine output decreases on hot summer days when electricity demand peaks. For GE-F5 gas turbines of Mashad Power Plant when ambient temperature increases 1° C, compressor outlet temperature increases 1.13° C and turbine exhaust temperature increases 2.5° C. Also air mass flow rate decreases about 0.6 kg/sec when ambient temperature increases 1° C, so it is revealed that variations are more due to decreasing in the efficiency of compressor and less due to reduction in mass flow rate of air as ambient temperature increases in constant power output. The cycle efficiency of these GE-F5 gas turbines reduces 3 percent with increasing 50° C of ambient temperature, also the fuel consumption increases as ambient temperature increases for constant turbine work. These are also because of reducing in the compressor efficiency in high temperature ambient. Steam injection in gas turbines is a way to prevent a loss in performance of gas turbines caused by high ambient temperature and has been used for many years. VODOLEY system is a steam injection system, which is known as a self-sufficient one in steam production. The amount of water vapor in combustion products will become regenerated in a contact condenser and after passing through a heat recovery boiler is injected in the transition piece after combustion chamber. In this paper the influence of steam injection in Mashad Power Plant GE-F5 gas turbine parameters, applying VODOLEY system, is being observed. Results show that in this turbine, the turbine inlet temperature (T3) decreases in a range of 5 percent to 11 percent depending on ambient temperature, so the operating parameters in a gas turbine cycle equipped with VODOLEY system in 40° C of ambient temperature is the same as simple gas turbine cycle in 10° C of ambient temperature. Results show that the thermal efficiency increases up to 10 percent, but Back-Work ratio increases in a range of 15 percent to 30 percent. Also results show that although VODOLEY system has water treatment cost but by using this system the running cost will reduce up to 27 percent.

Optimum Gain-Scheduling PID Controllers for Gas Turbine Engines Based on NARMAX and Neural Network Models

2003 ◽  
Author(s):  
Junxia Mu ◽  
David Rees ◽  
Neophytos Chiras
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Pid Controller ◽  
Network Models ◽  
Gain Scheduling ◽  
Pid Controllers ◽  
Neural Network Models ◽  
Turbine Engine ◽  
Dynamic Relationship ◽  
Fuel Flow

This paper presents PID controller designs based on NARMAX and feedforward neural network models of a Spey gas turbine engine. Both models represent the dynamic relationship between the fuel flow and shaft speed. Due to the engine non-linearity, a single set of PID controller parameters is not sufficient to control the gas turbine throughout the operating range. Gain-scheduling PID controllers are therefore used in order to obtain optimum control. A comparison between the controller designs based on the two model representations is also made.

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 The Effect of Thermal Matching on the Thermodynamic Performance of Gas Turbine and IC Engine Cogeneration Systems

1985 ◽  
Author(s):  
J. W. Baughn ◽  
N. Bagheri
Keyword(s):  
Gas Turbine ◽  
Combustion Engine ◽  
Full Range ◽  
Operating Conditions ◽  
Hot Water ◽  
Ic Engine ◽  
Thermal Output ◽  
Savings Rate ◽  
Thermodynamic Performance ◽  
Cogeneration System

Computer models have been used to analyze the thermodynamic performance of a gas turbine (GT) cogeneration system and an internal combustion engine (IC) cogeneration system. The purpose of this study was to determine the effect of thermal matching of the load (i.e., required thermal energy) and the output steam fraction (fraction of the thermal output, steam and hot water, which is steam) on the thermodynamic performance of typical cogeneration systems at both full and partial output. The thermodynamic parameters considered were; the net heat rate (NHR), the power to heat ratio (PHR), and the fuel savings rate (FSR). With direct use (the steam fractions being different); the NHR of these two systems is similar at full output, the NHR of the IC systems is lower at partial output, and the PHR and the FSR of the GT systems is lower than the IC systems over the full range of operating conditions. With thermal matching (to produce a given steam fraction) the most favorable NHR, PHR, and FSR depends on the method of matching the load to the thermal output.

Using Machine Learning to Increase Model Performance for a Gas Turbine System

2020 ◽  
Author(s):  
Samuel M. Hipple ◽  
Zachary T. Reinhart ◽  
Harry Bonilla-Alvarado ◽  
Paolo Pezzini ◽  
Kenneth Mark Bryden
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Gas Turbine ◽  
Clean Energy ◽  
Learning Model ◽  
Operating Conditions ◽  
Plant Performance ◽  
Outlet Temperature ◽  
Machine Learning Model ◽  
Critical Components

Abstract With increasing regulation and the push for clean energy, the operation of power plants is becoming increasingly complex. This complexity combined with the need to optimize performance at base load and off-design condition means that predicting power plant performance with computational modeling is more important than ever. However, traditional modeling approaches such as physics-based models do not capture the true performance of power plant critical components. The complexity of factors such as coupling, noise, and off-design operating conditions makes the performance prediction of critical components such as turbomachinery difficult to model. In a complex system, such as a gas turbine power plant, this creates significant disparities between models and actual system performance that limits the detection of abnormal operations. This study compares machine learning tools to predict gas turbine performance over traditional physics-based models. A long short-term memory (LSTM) model, a form of a recurrent neural network, was trained using operational datasets from a 100 kW recuperated gas turbine power system designed for hybrid configuration. The LSTM turbine model was trained to predict shaft speed, outlet pressure, and outlet temperature. The performance of both the machine learning model and a physics-based model were compared against experimental data of the gas turbine system. Results show that the machine learning model has significant advantages in prediction accuracy and precision compared to a traditional physics-based model when fed facility data as an input. This advantage of predicting performance by machine learning models can be used to detect abnormal operations.

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