Research on coordinated control system of drum boiler units considering energy demand decoupling

Gao, Yaokui; Zeng, Deliang; Boyu, Ping; Zhang, Lixia; Liu, Jizhen

doi:10.1016/j.conengprac.2020.104562

Cited by 13 publications

(2 citation statements)

References 31 publications

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“…In 2006, China introduced this technology for the first time and successfully completed the power coal wharf in Lianyungang port. The trial products of the Lianyungang CBF project of the China-Hong Kong-India group have been used in some power plants and have been proven to have good application effects by scientific research institutions such as the Thermal Coal Research Institute [9]. The reliability and accuracy of dynamic measurement and the accuracy of online detection of thermal coal are the key factors affecting the accuracy of thermal coal distribution.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Optimization Analysis of Power Coal‐Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm

Tao

Song²,

Zhu

et al. 2023

Journal of Sensors

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Research and optimization of the coal-blending system can greatly improve the variety diversity and quality stability of finished coal. However, the existing research on the coal-blending system has some defects, such as fuzzy ash content of coal-blending warehouse products and excessive error of the coal-blending ratio determined by artificial experience, which brings many disadvantages and difficulties to the efficient and stable production of the coal preparation plant. The genetic algorithm and intelligent sensor network are relatively advanced technologies at present. Therefore, the optimization analysis of the coal-blending model and its control system based on the intelligent sensor network and genetic algorithm has become a research hotspot. This paper first introduces the basic theory of the genetic algorithm and intelligent sensor network and their application in coal-blending research, then establishes an optimized dynamic coal-blending model based on the intelligent sensor network and genetic algorithm, and analyzes its application effect. The research results show that the coal quality prediction model can be mined from coal quality data and coal-blending data by using the genetic algorithm and ideas, and the monitoring system based on the intelligent sensor network can monitor the abnormal state anytime and anywhere. Compared with the traditional coal-blending method, the average error is 3.33%, and the accuracy is improved by 4.82%.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Optimization Analysis of Power Coal‐Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm

Tao

Song²,

Zhu

et al. 2023

Journal of Sensors

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show abstract

“…The MPC is a control theory that predicts the future output for a finite prediction horizon and calculates the optimized control input. In [8], a Generalized Predictive Control (GPC)-based CC is proposed. Dynamic Matrix Control (DMC) is also successfully adopted as a CC [9].…”

Section: Introductionmentioning

confidence: 99%

Supplementary Control of Conventional Coordinated Control for 1000 MW Ultra-Supercritical Thermal Power Plant Using One-Step Ahead Control

Choi,

Moon

et al. 2023

Energies

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The intermittence of renewable energy sources increases the importance of the effective load-tracking ability of power plants. Coordinated control between boiler and turbine systems is the uppermost layer of a thermal power plant control to follow the load demand. In this paper, a supplementary controller is proposed based on the One-Step Ahead strategy for coordinated control of thermal power plants. After a plant model is developed offline from a step response test, the optimized control of the One-Step Ahead strategy is applied to the boiler feed-forward (BFF) signal to control the electric power output and the main steam pressure simultaneously. Simulation with a 1000 MW ultra-supercritical (USC) once-through type power plant is performed. The results show that the error of Mega-Watt Output (MWO) was reduced to 78~95%, and settling time was reduced to 64~79% from conventional coordinated control by adding the proposed supplementary controller.

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A feature-state observer and suppression control for generation-side low-frequency oscillation of thermal power units

Hong,

Zhao,

et al. 2024

Applied Energy

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Research on coordinated control system of drum boiler units considering energy demand decoupling

Cited by 13 publications

References 31 publications

[Retracted] Optimization Analysis of Power Coal‐Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm

[Retracted] Optimization Analysis of Power Coal‐Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm

Supplementary Control of Conventional Coordinated Control for 1000 MW Ultra-Supercritical Thermal Power Plant Using One-Step Ahead Control

A feature-state observer and suppression control for generation-side low-frequency oscillation of thermal power units

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