Fuzzy Predictive Supervisory Control Based on Genetic Algorithms for Gas Turbines of Combined Cycle Power Plants

Sáez, Doris; Milla, Freddy; Vargas, Luis

doi:10.1109/tec.2007.902678

Cited by 24 publications

(8 citation statements)

References 21 publications

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“…6: Fuel system (fuel valve and actuator), Compressor, Combustor and Turbine. This model has been validated using real data [5]. …”

Section: Gas Turbine Modelmentioning

confidence: 99%

“…The gas turbine control scheme is adopted from [11], [19] and [5]. In this scheme, the main control loop is the speed governor.…”

Section: Gas Turbine Control Schemementioning

confidence: 99%

“…The application of this strategy to control CCPP has been widely presented and discussed in literature, [4], [5]. In this strategy the MPC provides the regulatory level optimum set-points, based on the objective function dynamic optimisation.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Supervisory Control Design for a Combined Cycle Power Plant

Abokhatwa¹,

Katebi²

2012

Software Engineering / 781: Control Applications

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The traditional control strategy based on PID controllers may be unsatisfactory when dealing with processes with large time delay and constraints. This paper presents a supervisory model based constrained predictive controller (MPC) for a combined cycle power plant (CCPP). First, a non-linear dynamic model of CCPP using the laws of physics was proposed. Then, the supervisory control using the linear constrained MPC method was designed to tune the performance of the PID controllers by including output constraints and manipulating the set points. This scheme showed excellent tracking and disturbance rejection results and improved performance compared with a stand-alone PID controller's scheme. KEY WORDSCombined cycle power plant, PID Control, Linearization, Supervisory Model Predictive Control.

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“…6: Fuel system (fuel valve and actuator), Compressor, Combustor and Turbine. This model has been validated using real data [5]. …”

Section: Gas Turbine Modelmentioning

confidence: 99%

“…The gas turbine control scheme is adopted from [11], [19] and [5]. In this scheme, the main control loop is the speed governor.…”

Section: Gas Turbine Control Schemementioning

confidence: 99%

See 1 more Smart Citation

Modeling and Supervisory Control Design for a Combined Cycle Power Plant

Abokhatwa¹,

Katebi²

2012

Software Engineering / 781: Control Applications

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“…the specific problem. Then people propose a method called Genetic Algorithm (GA) [5] to estimate parameter for nonlinear model. GA is widely used in many fields due to its highly parallel processing ability, strong robustness and global searching ability.…”

Section: Introductionmentioning

confidence: 99%

Power System Stabilizer Parameters Designing Based on Genetic Simulated Annealing Algorithm

Li¹,

Wang²,

Xu³

et al. 2015

JOCET

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Power system stabilizer (PSS) is one of the most effective measures to damp the power system low frequency oscillation, the entire parameter estimation is generally more complex and difficult. According to the measured generating set uncompensated phase frequency characteristics, this paper use the objective function which is minimizing the sum of the absolute value to design PSS parameters, and the absolute value is composed of the difference between the square of required compensation phase in excitation system and the square of phase provided by PSS. Accordingly, this paper puts forward a kind of PSS parameter design method based on genetic simulated annealing algorithm. The parameter design method is applied to a hydroelectric power station generator No. 1 of China South power grid and then the results of the simulation show that using PSS parameter design method based on genetic simulated annealing algorithm can effectively damp the power system low frequency oscillation, and at the same time, simulation results verify the accuracy and validity of the method.

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“…Reference [3] analytically verifies techniques for online optimization of multiple parameter setpoints. Reference [4] presents fuzzy supervisory predictive control based on genetic algorithms for gas turbines of combined cycle power plants. Reference [5] presents nonlinear multivariable supervisory predictive control method using neuro-fuzzy network.…”

Section: Introductionmentioning

confidence: 99%

A stair-like hierarchical predictive control strategy

Liu

Qian

2010

2010 8th World Congress on Intelligent Control and Automation

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Model predictive control(MPC) is to solve an optimization problem for a finite future at current time and to implement the first optimal control input as the current control input. To improve the performance of the Stirred Tank Reactor, a new method based on predictive hierarchical control was introduced. As a modification of the regulatory controller usually implies higher costs, it is a common practice in the industry to add a supervisory level instead. The supervisory level dynamically optimizes a general objective function to determine the optimal set-point for a given regulatory level. According to the complexity of online computation, a hierarchical predictive scheme based on stair-like control was proposed. It approximated the future set-point sequences by a set of stair-like controllers per sampling time. The method decreased dimensions of the variables, so it could greatly reduce the online computation burden and prevent the frequent oscillation of the input. Simulation results prove the effectiveness of the proposed controller.

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Fuzzy Predictive Supervisory Control Based on Genetic Algorithms for Gas Turbines of Combined Cycle Power Plants

Cited by 24 publications

References 21 publications

Modeling and Supervisory Control Design for a Combined Cycle Power Plant

Modeling and Supervisory Control Design for a Combined Cycle Power Plant

Power System Stabilizer Parameters Designing Based on Genetic Simulated Annealing Algorithm

A stair-like hierarchical predictive control strategy

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