Adaptive hybrid predictive control for a combined cycle power plant optimization

Sáez, Doris; Zúñiga, Roberto Ariel Abeldaño; Cipriano, Aldo

doi:10.1002/acs.988

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…The recent need for plant flexibility has led to an increasing interest in applying the MPC to onshore power plants. As an example, Sáez et al [13] employed a MPC unit to control the start-up, normal operation, and shut-down of a combined cycle power plant. The proposed algorithm, tested in a model of a real system, improved the tracking capacities and decreased the fuel consumption by 3 % [13].…”

Section: Introductionmentioning

confidence: 99%

“…As an example, Sáez et al [13] employed a MPC unit to control the start-up, normal operation, and shut-down of a combined cycle power plant. The proposed algorithm, tested in a model of a real system, improved the tracking capacities and decreased the fuel consumption by 3 % [13]. Quoilin et al [14] developed a dynamic model of an ORC unit based on first principles.…”

Section: Introductionmentioning

confidence: 99%

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Model Predictive Control of Offshore Power Stations With Waste Heat Recovery

Pierobon

Chan²,

et al. 2016

Journal of Engineering for Gas Turbines and Power

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The implementation of waste heat recovery units on oil and gas offshore platforms demands advances in both design methods and control systems. Model-based control algorithms can play an important role in the operation of offshore power stations. A novel regulator based on a linear model predictive control (MPC) coupled with a steady-state performance optimizer has been developed in the simulink language and is documented in the paper. The test case is the regulation of a power system serving an oil and gas platform in the Norwegian Sea. One of the three gas turbines is combined with an organic Rankine cycle (ORC) turbogenerator to increase the energy conversion efficiency. Results show a potential reduction of frequency drop up to 40% for a step in the load set-point of 4 MW, compared to proportional–integral control systems. Fuel savings in the range of 2–3% are also expected by optimizing on-the-fly the thermal efficiency of the plant.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model Predictive Control of Offshore Power Stations With Waste Heat Recovery

Pierobon

Chan²,

et al. 2016

Journal of Engineering for Gas Turbines and Power

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“…The steam turbine control scheme used in this paper is based on boiler following mode as in [21]. As shown in Fig.…”

Section: Steam Turbine Control Schemementioning

confidence: 99%

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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“…Sáez et al [6] propose an adaptive hybrid predictive controller for the optimization of a real combined cycle power plant. Start modes, minimum up/down times and other logical features are represented using mixed integer equations, and dynamic behaviour of the ambient temperature is represented using adaptive fuzzy models.…”

mentioning

confidence: 99%