Exploitation of advanced control techniques in power generation

Oluwande, G.

doi:10.1049/cce:20010203

Cited by 7 publications

(2 citation statements)

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“…Model predictive control is a well recognized control system technology for controlling power plants and many industrial processes (Bittani and Poncia 2003 ; Mohamed et al 2012 , Oluwande 2001 ; Badgwell and Qina 2003 ). Although there are many other modern control techniques in the previously published literature (Lee and Ramirez 2001 ), state space formulation of multivariable model based predictive control has been selected for this specific application for so many reasons.…”

Section: Predictive Controller Design and Implementationmentioning

confidence: 99%

Predictive control strategy of a gas turbine for improvement of combined cycle power plant dynamic performance and efficiency

et al. 2016

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This paper presents a novel strategy for implementing model predictive control (MPC) to a large gas turbine power plant as a part of our research progress in order to improve plant thermal efficiency and load–frequency control performance. A generalized state space model for a large gas turbine covering the whole steady operational range is designed according to subspace identification method with closed loop data as input to the identification algorithm. Then the model is used in developing a MPC and integrated into the plant existing control strategy. The strategy principle is based on feeding the reference signals of the pilot valve, natural gas valve, and the compressor pressure ratio controller with the optimized decisions given by the MPC instead of direct application of the control signals. If the set points for the compressor controller and turbine valves are sent in a timely manner, there will be more kinetic energy in the plant to release faster responses on the output and the overall system efficiency is improved. Simulation results have illustrated the feasibility of the proposed application that has achieved significant improvement in the frequency variations and load following capability which are also translated to be improvements in the overall combined cycle thermal efficiency of around 1.1 % compared to the existing one.

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Section: Predictive Controller Design and Implementationmentioning

confidence: 99%

Predictive control strategy of a gas turbine for improvement of combined cycle power plant dynamic performance and efficiency

et al. 2016

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“…Thirdly, the control strategy implemented as well as the hardware employed to perform this task, i.e. the boiler design, water and steam piping design, the instrumentation design and calibration, final control elements (valves), etc., also have their limitations in achieving effective control of the water level in the boiler drum [22].…”

Section: Introductionmentioning

confidence: 99%

Neural Networks-based Process Model and its Integration with Conventional Drum Level PID Control in a Steam Boiler Plant

Mugweni¹,

Harb²

2021

IJEM

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Distribution-based adaptive bounding genetic algorithm for continuous optimisation problems

Jia

Thompson

et al. 2007

Applied Mathematics and Computation

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Exploitation of advanced control techniques in power generation

Cited by 7 publications

References 0 publications

Predictive control strategy of a gas turbine for improvement of combined cycle power plant dynamic performance and efficiency

Predictive control strategy of a gas turbine for improvement of combined cycle power plant dynamic performance and efficiency

Neural Networks-based Process Model and its Integration with Conventional Drum Level PID Control in a Steam Boiler Plant

Distribution-based adaptive bounding genetic algorithm for continuous optimisation problems

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