Flexible Optimal Control of the CFBB Combustion System Based on ESKF and MPC.

IF 3.5 3区 综合性期刊 Q2 CHEMISTRY, ANALYTICAL Sensors Pub Date : 2025-02-19 DOI:10.3390/s25041262
Lei Han, Lingmei Wang, Enlong Meng, Yushan Liu, Shaoping Yin
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Abstract

In order to deeply absorb the power generation of new energy, coal-fired circulating fluidized bed units are widely required to participate in power grid dispatching. However, the combustion system of the units faces problems such as decreased control performance, strong coupling of controlled signals, and multiple interferences in measurement signals during flexible operation. To this end, this paper proposes a model predictive control (MPC) scheme based on the extended state Kalman filter (ESKF). This scheme optimizes the MPC control framework. The ESKF is used to filter the collected output signals and jointly estimate the state and disturbance quantities in real time, thus promptly establishing a prediction model that reflects the true state of the system. Subsequently, taking the minimum output signal deviation of the main steam pressure and bed temperature and the control signal increment as objectives, a coordinated receding horizon optimization is carried out to obtain the optimal control signal of the control system within each control cycle. Tracking, anti-interference, and robustness experiments were designed to compare the control effects of ESKF-MPC, ID-PI, ID-LADRC, and MPC. The research results show that, when the system parameters had a ±30% perturbation, the adjustment time range of the main steam pressure and bed temperature loops of this method were 770~1600 s and 460~1100 s, respectively, and the ITAE indicator ranges were 0.615 × 105~1.74 × 105 and 3.9 × 106~6.75 × 106, respectively. The overall indicator values were smaller and more concentrated, and the robustness was stronger. In addition, the test results of the actual continuous variable condition process of the unit show that, compared with the PI strategy, after adopting the ESKF-MPC strategy, the overshoot of the main steam pressure loop of the combustion system was small, and the output signal was stable; the fluctuation range of the bed temperature loop was small, and the signal tracking was smooth; the overall control performance of the system was significantly improved.

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基于ESKF和MPC的循环流化床燃烧系统柔性最优控制。
为了深度吸收新能源发电,燃煤循环流化床机组被广泛要求参与电网调度。但机组燃烧系统在灵活运行过程中存在控制性能下降、被控信号耦合强、测量信号多重干扰等问题。为此,本文提出了一种基于扩展状态卡尔曼滤波(ESKF)的模型预测控制方案。该方案对MPC控制框架进行了优化。利用ESKF对采集到的输出信号进行滤波,实时联合估计状态和扰动量,从而迅速建立反映系统真实状态的预测模型。随后,以主蒸汽压力、床层温度输出信号偏差最小和控制信号增量最小为目标,进行协调退层优化,得到控制系统在每个控制周期内的最优控制信号。设计跟踪、抗干扰和鲁棒性实验,比较ESKF-MPC、ID-PI、ID-LADRC和MPC的控制效果。研究结果表明,当系统参数受±30%扰动时,该方法对主蒸汽压力回路和床层温度回路的调节时间范围分别为770~1600 s和460~1100 s, ITAE指标范围分别为0.615 × 105~1.74 × 105和3.9 × 106~6.75 × 106。总体指标值越小越集中,稳健性越强。另外,对机组实际连续变工况过程的测试结果表明,与PI策略相比,采用ESKF-MPC策略后,燃烧系统主蒸汽压力回路超调量小,输出信号稳定;床层温度回路波动幅度小,信号跟踪平稳;系统的整体控制性能得到显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Sensors
Sensors 工程技术-电化学
CiteScore
7.30
自引率
12.80%
发文量
8430
审稿时长
1.7 months
期刊介绍: Sensors (ISSN 1424-8220) provides an advanced forum for the science and technology of sensors and biosensors. It publishes reviews (including comprehensive reviews on the complete sensors products), regular research papers and short notes. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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