太阳能热发电定日镜集群的追日聚光策略与优化控制算法研究

项目名称： 太阳能热发电定日镜集群的追日聚光策略与优化控制算法研究

项目编号： No.61273100

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 黄文君

作者单位： 浙江大学

项目金额： 80万元

中文摘要： 塔式太阳能热发电是大型太阳能发电中最具市场前景的发电方式，本项目针对该系统中大规模定日镜集群追日聚光控制难度大、动态精度差等问题，以基于通讯网络调度由大量独立两轴转动的单元平面镜构成的聚光集热定日镜集群为研究对象，研究聚光单元平面镜的运动机理和特性，建立其非线性、多维度和多参数的空间运动模型方程；提出基于光斑参照系统结合机器视觉的光斑定位法获得单元平面镜的运动轨迹信息，利用非线性优化计算获得最佳光斑拟合，实现对单元平面镜的空间运动模型方程参数的自动定期智能校正，解决定日镜集群中单元平面镜的精确追日以及随机械磨损等内在特性改变和环境、季节等外界因素变化造成追日聚光精度的漂移问题，实现定日镜集群整体聚光和高效率能量收集，通过上述研究、仿真计算并实验验证，提出定日镜集群的追日聚光优化控制策略、旨在建立一套定日镜集群建模、校正和优化控制的科学体系，为实现高效率的大规模塔式太阳能热发电奠定技术基础。

中文关键词： 太阳能热发电；定日镜集群；建模；自动智能校正；追日聚光和优化控制

英文摘要： Solar tower power plant is the widest market prospect type in large-scale solar power plants. To solve the problems in the sun-tracking and concentrating control and low dynamic precision of the heliostat clusters system, the large-scale heliostat clusters which consist of mass two-axes rotation mirrors and scheduled by communication network are researched in this project. The nonlinear, multidimensional, and multiparameter kinematic model of a single mirror is developed based on analysis of the mechanism and property. The motion trajectory information of a single mirror is obtained by using sun's image reference system combined with machine vision-based method. Nonlinear optimization is proposed to get the best sun's image fitting to achieve the mirror parameters automatic termly intelligent calibration. The calibrations overcome the sun-tracking and concentrating precision drift caused by mechanical wears of the mirror,the environmental and seasonal variations and achieve the high focusing rate and high efficiency of the energy collecting of the large-scale heliostat clusters. Through the theoretical and experimental researches, the modelling, calibration, and optimal control theory and system of heliostat clusters are set up, which provide the technological foundation to achieve high efficiency utilization

英文关键词： Solar power plant；Heliostat clusters；Modelling；Automatic intelligent calibration；Sun-tracking and concentrating optimization