太阳能电池板自动跟踪及蓄电池充电系统研究

发布时间：2018-08-06 20:20

【摘要】：面临化石能源日益紧缺的现状，太阳能成为新能源角逐场的有力竞争者，而太阳能发电是太阳能利用的一条主要途径。但是在全世界光伏装机激增的环境下，电池板转换效率低下却是阻挡其生活化发展道路上的巨大障碍，多年来人们一直致力于提高光伏发电效率。首先，为了在每天太阳东升西落的变化中得到最强的光照强度，人们提出了太阳追踪系统。只是单轴计时追踪不符合长时间太阳相对地球的运动规律，而且现有的双轴逐日追踪精确度不高。其次，通常的最大功率跟踪方法会因为电池板外部环境的变化出现误跟踪。最后，因为光伏发电电流不稳定和充电时机的稍纵即逝，都迫使系统需要一个智能可靠的充电系统对蓄电池安全快速的充电。快速充电的大电流和安全充电的小电流之间的平衡，应根据蓄电池的状态进行智能控制。根据以上问题，本文的研究工作分如下三方面：（1）依据电池板的输出曲线可以看出，光照强度越强，光伏电池板的输出功率就越大。本文以光照强度作为控制参数，设计了太阳追踪系统，以此来提高每时刻光伏发电量。采用模糊控制的双轴追踪法对太阳光线进行追踪，以太阳运动过程中光线垂直照射在电池板表面为目标。（2）电池板的最大输出功率会因为温度和光照强度的变化而产生偏移，导致最大功率跟踪系统的控制难度增大。本文在改进后粒子群算法的指导下，展开对电池板最大功率的搜索。采用改进粒子群算法下的跟踪系统，可以准确搜索到变化环境下光伏电池板的最大值点。（3）本文根据马斯曲线采用阶段式恒流充电法，并结合瞬间反向放电的方式，设计了蓄电池快速充电系统。由于电池板输出的不稳定，使得常规充电法无法解决系统中的蓄电池充电问题。本文采用在输出电流较小时直接充电，较大时快速充电，直到即将充满时转为浮充方法充电的策略，，可以对蓄电池快速安全充电。本文根据光伏发电所存在的以上问题分别从太阳追踪、最大功率跟踪以及高效安全充电方面进行了分析研究。结果表明，在高速度和资源丰富的FPGA(现场可编程门阵列)支持下，模糊控制的双轴追踪系统能在天气晴朗的情况下，使太阳光与电池板垂直偏差不超过3度；在粒子群算法调节下的电池板，在1秒内能跟踪到最大功率值点；采用阶段恒流快速充电的智能充电系统，能将65AH的蓄电池充电时间缩短到1-2小时。
[Abstract]:In the face of the increasing shortage of fossil energy, solar energy has become a strong competitor in the new energy competition field, and solar power generation is one of the main ways to use solar energy. However, in the environment of worldwide photovoltaic equipment proliferation, the low efficiency of panel conversion is a huge obstacle to the development of photovoltaic power generation. People have been working to improve the efficiency of photovoltaic power generation for many years. First, in order to obtain the strongest light intensity in the daily variation of the sun's rise and fall, a solar tracking system is proposed. But the uniaxial tracking does not accord with the motion of the sun relative to the earth for a long time, and the existing biaxial tracking accuracy is not high. Secondly, the usual maximum power tracking method may mistrack because of the change of the external environment of the battery panel. Finally, because of the instability of photovoltaic generation current and the fleeting of the charging time, the system needs an intelligent and reliable charging system to charge the battery safely and quickly. The balance between the high current of fast charging and the low current of safe charging should be controlled intelligently according to the state of battery. According to the above problems, the research work of this paper is as follows: (1) according to the output curve of the panel, it can be seen that the stronger the light intensity, the greater the output power of the photovoltaic panel. In this paper, the solar tracking system is designed with the illumination intensity as the control parameter to improve the photovoltaic power generation at every moment. A biaxial tracking method based on fuzzy control is used to track solar rays. In the process of solar motion, the light shone perpendicular to the surface of the panel. (2) the maximum output power of the panel will be offset by the change of temperature and illumination intensity, which makes the control of the maximum power tracking system more difficult. Under the guidance of the improved particle swarm optimization (PSO) algorithm, the maximum power of the battery panel is searched in this paper. Based on the improved particle swarm optimization (PSO) tracking system, the maximum point of photovoltaic panels can be accurately found. (3) according to the Mas curve, the phase constant current charging method is adopted and the instantaneous reverse discharge is combined. The rapid charging system of battery is designed. Because of the instability of the battery panel output, the conventional charging method can not solve the battery charging problem in the system. This paper adopts the strategy of direct charging when the output current is small, and fast charging when the output current is large, until the charging process is about to be filled, which can be used to charge the battery quickly and safely. In this paper, solar tracking, maximum power tracking and efficient and safe charging are analyzed according to the above problems of photovoltaic power generation. The results show that under the support of FPGA (Field Programmable Gate Array) with high speed and abundant resources, the fuzzy control biaxial tracking system can make the vertical deviation between solar light and battery plate less than 3 degrees in sunny weather. The maximum power point can be traced to the battery board adjusted by particle swarm optimization algorithm in one second, and the charging time of 65AH battery can be shortened to 1-2 hours by using the intelligent charging system with stage constant current and fast charging.
【学位授予单位】：重庆理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM914.4;TM912

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