An Efficient Robust Predictive Control of Main Steam Temperature of Coal-Fired Power Plant

This document provides the motivations and a brief introduction to the Special Issue entitled “Advanced Control Design and Fault Diagnosis”, which aims at presenting several solutions to the advanced control design and fault diagnosis systems. These methodologies can be considered in the general framework of advanced control, fault diagnosis and fault tolerant control systems, which are also able to improve the safety of the system under monitoring. The focuses of the current research in this field addressed in this Special Issue are also presented with emphasis on the practical application to simulated and realistic examples, which should provide an overall picture of current and future developments in this area. The works of this Special Issue represent suitably extended contributions selected by the proponents from the ACD2019—the 15th European Workshop on Advanced Control and Diagnosis, which was organised in Bologna, Italy on November 21st–22nd.

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“…• An Efficient Robust Predictive Control of Main Steam Temperature of Coal-Fired Power Plant by Di Wang, Xiao Wu and Jiong Shen [11].…”

Section: Contributions' Descriptionmentioning

confidence: 99%

Advanced Control Design and Fault Diagnosis

Simani

Zattoni

2021

Energies

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“…The parameter perturbations always exist in actual power plants and affect the coordinated control performance. Generally speaking, common control methods such as active disturbance rejection control, 30,31 robust control, [32][33][34] output feedback integral control, 35 and MPC 13,36 have certain robustness and anti-interference ability so that the influence of parameter perturbations on outputs can be eliminated by adjusting the inputs. For the complex controlled object with large inertia and large time delays like power plants, although the system can maintain stable operation, the control performance will inevitably deteriorate.…”

Section: Introductionmentioning

confidence: 99%

Digital twin-based subspace model predictive control for thermal power plant

Zhao

Cai

Jiang

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, a digital twin-based subspace model predictive control scheme is proposed for solving model mismatch caused by parameter perturbations of thermal power plants. First, the rational range of essential parameters in the nonlinear model is identified based on the nominal data of an actual 600 MW power plant, which provides necessary foundation for subsequent studies. Second, to improve the load-changing capacity under parameter disturbances, a novel digital twin-based subspace model predictive control algorithm is established by regarding the crucial parameter as extended input of predictive model. Finally, the small fluctuations during steady-state operation are effectively restrained through transforming weights and constraints slowly and smoothly at the end of load change. Simulation results illustrate that compared with the conventional subspace-based model predictive control, the proposed digital twin-based subspace model predictive control scheme can obtain a better performance in terms of both dynamic regulation rate and steady-state performance under parameter perturbations.

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“…Recent trends in power plant and industrial applications indicate developments like data reconciliation (DR), Guo et al [1] model predictive control (MPC), Wang et al [2] and optimization, Niegodajew et al [3] to improve performance of plants and processes. They are based on the first principle thermodynamic formulations as well complex mathematical models.…”

Section: Introductionmentioning

confidence: 99%

Neural net implementation of steam properties on FPGA

Dutt¹,

Ganesh²,

Premchand

2021

IJRES

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Real time applications like model predictive control, monitoring and data reconciliation of power plants and industrial processes employ nonlinear mathematical models and require thermodynamic properties and their derivatives of working fluids. Applications like super heater temperature control based on energy balance and real time data reconciliation, require an efficient and a compact method for simultaneous estimation of thermodynamic properties, and their partial derivatives suitable for implementation in field-programmable gate array (FPGA). However, the complex mathematical formulations of these properties prohibit direct implementations in FPGAs. Single artificial neural network (ANN) architecture is used to replace the entire code in higher level languages, running into a few thousand lines. FPGA implementation of a compact neural network for the entire range of thermodynamic properties is presented. Large arguments in sigmoid function are factored into a product of integer and a fractional part which is represented using series approximation with five terms only and the integers are represented in look up table (LUT). This ensures optimum storage and computational burden for the above applications. The ANN is implemented in IEEE 754 floating point with synthesis in Xilinx ISE design suite using Verilog HDL. The results are presented for a typical pressure versus saturation temperature.

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An Efficient Robust Predictive Control of Main Steam Temperature of Coal-Fired Power Plant

Cited by 8 publications

References 33 publications

Advanced Control Design and Fault Diagnosis

Advanced Control Design and Fault Diagnosis

Digital twin-based subspace model predictive control for thermal power plant

Neural net implementation of steam properties on FPGA

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