基于自适应鲁棒Kalman滤波的吸热器出口熔盐温度预测控制方法

邵旻; 杨晓雷; 曹植; 陈志强; 陆骏阳; 张权锐; 李益国

doi:10.19805/j.cnki.jcspe.2026.250108

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

基于自适应鲁棒Kalman滤波的吸热器出口熔盐温度预测控制方法

数字化与智能化 | 更新时间：2026-07-16

- 基于自适应鲁棒Kalman滤波的吸热器出口熔盐温度预测控制方法
- Adaptive Robust Kalman Filtering Based Predictive Control Method of Outlet Molten Salt Temperature of Receiver
- 动力工程学报 2026年46卷第7期页码：96-103
- 作者机构：
  
  1. 山东电力工程咨询院有限公司,山东,济南,250013
  2. 东南大学能源与环境学院,江苏,南京,210096
- 作者简介：
  
  邵旻(1981—),女,山东郓城人,硕士,高级工程师,研究方向为发电厂仪表及控制系统、信息系统。李益国(通信作者),男,教授,博士,E-mail:lyg@seu.edu.cn。
- 基金信息：
  
  国家自然科学基金资助项目(52076038)
- DOI：10.19805/j.cnki.jcspe.2026.250108
  中图分类号： TK513;TP273
- 纸质出版：2026
- 稿件说明：
移动端阅览
邵旻, 杨晓雷, 曹植, 陈志强, 陆骏阳, 张权锐, 李益国. 基于自适应鲁棒Kalman滤波的吸热器出口熔盐温度预测控制方法[J]. 动力工程学报, 2026, 46(7): 96-103.
邵旻, 杨晓雷, 曹植, 陈志强, 陆骏阳, 张权锐, 李益国. 基于自适应鲁棒Kalman滤波的吸热器出口熔盐温度预测控制方法[J]. 动力工程学报, 2026, 46(7): 96-103. DOI： 10.19805/j.cnki.jcspe.2026.250108.

DOI： 10.19805/j.cnki.jcspe.2026.250108

摘要

针对塔式光热发电系统的吸热器出口熔盐温度因大迟延、非线性特性而难以通过常规比例积分微分(PID)控制算法取得满意控制效果的工程问题

提出了一种基于自适应鲁棒Kalman滤波的吸热器出口熔盐温度预测控制方法。该方法通过引入模型误差容限

并自适应更新测量噪声协方差矩阵

处理预测模型与实际对象的失配问题

从而提高控制系统的鲁棒性。针对太阳辐射量大范围扰动的仿真结果表明

所提方法的超调量相较于基于鲁棒Kalman滤波的预测控制、基于标准Kalman滤波的预测控制及动态矩阵控制(DMC)分别降低了2.45、4.74和12.43 K

对应下降幅度依次为7.37%、12.48%与27.21%。在各类扰动工况下

所提方法均展现出更优的动态偏差抑制效果与更快的调节速度

充分验证了自适应鲁棒Kalman滤波策略应对大迟延、非线性对象模型失配问题的有效性。

Abstract

For the engineering problem where satisfactory control of the outlet molten salt temperature of the receiver in a tower solar thermal power system is difficult to achieve with using conventional proportional integral derivative (PID) control algorithms due to large time delay and nonlinear characteristics

a predictive control method for the outlet molten salt temperature of the receiver based on adaptive robust Kalman filtering was proposed. In this method

the mismatch between the prediction model and the actual object was addressed by introducing a model error tolerance bound and adaptively updating the measurement noise covariance matrix

thus enhancing the robustness of the control system. Simulation results under large-magnitude solar radiation disturbances show that

compared with predictive control based on robust Kalman filtering

predictive control based on standard Kalman filtering

and dynamic matrix control (DMC)

the overshoot of the proposed method is reduced by 2.45

4.74

and 12.43 K

respectively

with corresponding reduction percentages of 7.37%

12.48%

and 27.21%. Under various disturbance conditions

the proposed method is demonstrated to exhibit superior dynamic deviation suppression and faster adjustment speed

fully validating the effectiveness of the adaptive robust Kalman filtering strategy in addressing the large time delay and model mismatch problems of nonlinear objects.

关键词

Keywords

references

李学来, 贺博. 太阳能光热电站发展展望[J]. 能源与环境, 2017(6): 75-77.LI Xuelai, HE Bo. Prospects for the development of solar thermal power plants[J]. Energy and Environment, 2017(6): 75-77.

杜宇航, 刘向民, 王兴平, 等. 塔式光热电站定日镜不同聚焦策略的影响分析[J]. 动力工程学报, 2020, 40(5): 426-432.DU Yuhang, LIU Xiangmin, WANG Xingping, et al. Influence analysis on different concentrating strategies of heliostats in a tower solar thermal power station[J]. Journal of Chinese Society of Power Engineering, 2020, 40(5): 426-432.

蒋志浩, 杜宇航, 刘向民. 塔式光热电站定日镜场清洗策略优化研究[J]. 动力工程学报, 2020, 40(11): 924-928.JIANG Zhihao, DU Yuhang, LIU Xiangmin. Optimization of cleaning strategies for heliostat fields in tower solar thermal power plants[J]. Journal of Chinese Society of Power Engineering, 2020, 40(11): 924-928.

徐二树, 黄钰琛, 孔艳强, 等. 塔式太阳能光热发电系统蒸发器动态特性研究[J]. 动力工程学报, 2023, 43(6): 710-716.XU Ershu, HUANG Yuchen, KONG Yanqiang, et al. Study on dynamic characteristics of steam generator in solar tower power generation system[J]. Journal of Chinese Society of Power Engineering, 2023, 43(6): 710-716.

ZHANG Qiang, JIANG Kaijun, GE Zhihua, et al. Control strategy of molten salt solar power tower plant function as peak load regulation in grid[J]. Applied Energy, 2021, 294: 116967.

李佳燕. 太阳能热发电系统接收器的建模仿真及控制算法研究[D]. 杭州: 浙江大学, 2015.

GALLEGO A J, YEBRA L J, CAMACHO E F. Gain scheduling model predictive control of the new TCP-100 parabolic trough field[J]. IFAC-PapersOnLine, 2018, 51(2): 475-480.

ALSHARKAWI A, ROSSITER J A. Towards an improved gain scheduling predictive control strategy for a solar thermal power plant[J]. IET Control Theory & Applications, 2017, 11(12): 1938-1947.

RAO C V, WRIGHT S J, RAWLINGS J B. Application of interior-point methods to model predictive control [J]. Journal of Optimization Theory and Applications, 1998, 99(3): 723-757.

王浩坤, 徐祖华, 赵均, 等. 无偏模型预测控制综述[J]. 自动化学报, 2020, 46(5): 858-877.WANG Haokun, XU Zuhua, ZHAO Jun, et al. A survey on offset-free model predictive control[J]. Acta Automatica Sinica, 2020, 46(5): 858-877.

ZENERE A, ZORZI M. Model predictive control meets robust Kalman filtering[J]. IFAC-PapersOnLine, 2017, 50(1): 3774-3779.

GAMSE S, NOBAKHT-ERSI F, SHARIFI M. Statistical process control of a Kalman filter model[J]. Sensors, 2014, 14(10): 18053-18074.

SAGE A P, HUSA G W. Adaptive filtering with unknown prior statistics[J]. IEEE Transactions on Automatic Control, 1969(7): 760-769.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于动态变增益卡尔曼滤波的SCR无偏模型预测控制

基于扰动主动利用的孤岛微电网双模模型预测负荷频率控制

基于模型预测控制的混合储能平抑风电波动控制策略

热泵储能技术研究综述

无烟煤粉掺混小麦秸秆燃烧反应动力学研究