太阳能道路照明智能控制系统研究

发布时间：2018-05-09 02:04

本文选题：太阳能路灯 + MPPT充电　；参考：《哈尔滨理工大学》2016年硕士论文

【摘要】：随着化石能源的日益衰竭,能源危机和环境污染愈加严重,太阳能作为一种取之不尽,用之不竭的可再生清洁能源,用之取代化石能源具有重要的意义。太阳能道路照明系统作为太阳能利用的一种方式,被广泛应用照明领域。由于蓄电池使用寿命常常达不到设计要求,是整个系统中最薄弱的环节,所以加强能量管理,尤其是对蓄电池的充放电控制尤其重要。再则,目前的控制器控制策略单一,放电时间偏短,难以在连续阴雨天或高寒等恶劣环境下持续放电。因此,研发能量管理高效、智能化程度高的控制器具有广泛的应用价值。为了能对蓄电池高效充电,本文在太阳能光伏电池和铅酸蓄电池数学模型的基础上,分析了最大功率点跟踪(MPPT)技术,并对蓄电池充电策略进行优化补充。另外,针对目前的控制器放电策略不够完善,难以自如应对连续阴雨天或高寒等恶劣天气的情况,本文深入分析了环境温度、蓄电池剩余电量、天气状况等因素对系统照明时间的影响,并给出了一种基于人工神经网络算法的解决方案。本文给出了一个基于改进型蚁群-BP网络算法的数学模型,并对该模型进行了数学分析。将该模型应用于本文设计的放电控制策略中,并对它的准确性进行了MATLAB仿真分析。然后,搭建了一个以Atmega8L为控制核心实验平台,该平台包括:Buck型充电电路,控制负载运行的驱动电路和其他外围电路,以及满足相应功能要求的软件程序。最后,对本课题设计的控制器与北京日月升科技公司生产的RYS-JK71控制器进行性能比较,根据测试结果,采用MPPT优化算法的控制器在充电效率上高于RYS-JK71控制器,以及在连续阴雨天、高寒等恶劣气候条件下,本控制器的放电时间比RYS-Jk71长大约1/3,使得控制器具有更强的适应性,且能在恶劣环境下稳定可靠运行。
[Abstract]:With the increasing depletion of fossil energy, energy crisis and environmental pollution become more and more serious, solar energy as an inexhaustible renewable clean energy, it is of great significance to replace fossil energy. As a way of solar energy utilization, solar road lighting system is widely used in lighting field. Since battery life is often not up to the design requirements, it is the weakest link in the whole system, so it is very important to strengthen the energy management, especially to control the charge and discharge of the battery. Moreover, the current controller control strategy is single, the discharge time is short, it is difficult to discharge continuously in the severe environment such as continuous rainy day or high cold. Therefore, the research and development of energy management and intelligent controller has a wide range of application value. In order to charge the battery efficiently, based on the mathematical model of solar photovoltaic cell and lead-acid battery, the MPPTT technology of maximum power point tracking is analyzed, and the battery charging strategy is optimized and supplemented. In addition, in view of the current controller discharge strategy is not perfect, it is difficult to deal with the situation of continuous rainy weather or high and cold weather, this paper deeply analyzes the ambient temperature, battery residual electricity, etc. The influence of weather conditions on the lighting time of the system is discussed, and a solution based on artificial neural network (Ann) algorithm is presented. In this paper, a mathematical model based on improved ant colony BP network algorithm is presented, and the mathematical analysis of the model is given. The model is applied to the discharge control strategy designed in this paper, and its accuracy is analyzed by MATLAB simulation. Then, an experimental platform with Atmega8L as the control core is built. The platform includes the charging circuit of the type of: Buck, the driving circuit and other peripheral circuits to control the load running, and the software program to meet the corresponding functional requirements. Finally, the performance of the controller designed in this paper is compared with that of the RYS-JK71 controller produced by Beijing Sunrise Technology Company. According to the test results, the charge efficiency of the controller using the MPPT optimization algorithm is higher than that of the RYS-JK71 controller, as well as the continuous overcast and rainy days. The discharge time of the controller is about 1 / 3 longer than that of RYS-Jk71 under severe weather conditions such as high and cold conditions, which makes the controller more adaptable and can operate stably and reliably in bad environment.
【学位授予单位】：哈尔滨理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP273.5

【相似文献】