太陽能電池板自動跟蹤及蓄電池充電系統(tǒng)研究
發(fā)布時間：2018-08-06 20:20
【摘要】：面臨化石能源日益緊缺的現(xiàn)狀，太陽能成為新能源角逐場的有力競爭者，而太陽能發(fā)電是太陽能利用的一條主要途徑。但是在全世界光伏裝機激增的環(huán)境下，電池板轉換效率低下卻是阻擋其生活化發(fā)展道路上的巨大障礙，多年來人們一直致力于提高光伏發(fā)電效率。首先，為了在每天太陽東升西落的變化中得到最強的光照強度，人們提出了太陽追蹤系統(tǒng)。只是單軸計時追蹤不符合長時間太陽相對地球的運動規(guī)律，而且現(xiàn)有的雙軸逐日追蹤精確度不高。其次，通常的最大功率跟蹤方法會因為電池板外部環(huán)境的變化出現(xiàn)誤跟蹤。最后，因為光伏發(fā)電電流不穩(wěn)定和充電時機的稍縱即逝，都迫使系統(tǒng)需要一個智能可靠的充電系統(tǒng)對蓄電池安全快速的充電�？焖俪潆姷拇箅娏骱桶踩潆姷男‰娏髦g的平衡，應根據(jù)蓄電池的狀態(tài)進行智能控制。 根據(jù)以上問題，本文的研究工作分如下三方面： （1）依據(jù)電池板的輸出曲線可以看出，光照強度越強，光伏電池板的輸出功率就越大。本文以光照強度作為控制參數(shù)，設計了太陽追蹤系統(tǒng)，以此來提高每時刻光伏發(fā)電量。采用模糊控制的雙軸追蹤法對太陽光線進行追蹤，以太陽運動過程中光線垂直照射在電池板表面為目標。 （2）電池板的最大輸出功率會因為溫度和光照強度的變化而產(chǎn)生偏移，導致最大功率跟蹤系統(tǒng)的控制難度增大。本文在改進后粒子群算法的指導下，展開對電池板最大功率的搜索。采用改進粒子群算法下的跟蹤系統(tǒng)，可以準確搜索到變化環(huán)境下光伏電池板的最大值點。 （3）本文根據(jù)馬斯曲線采用階段式恒流充電法，并結合瞬間反向放電的方式，設計了蓄電池快速充電系統(tǒng)。由于電池板輸出的不穩(wěn)定，使得常規(guī)充電法無法解決系統(tǒng)中的蓄電池充電問題。本文采用在輸出電流較小時直接充電，較大時快速充電，直到即將充滿時轉為浮充方法充電的策略，，可以對蓄電池快速安全充電。 本文根據(jù)光伏發(fā)電所存在的以上問題分別從太陽追蹤、最大功率跟蹤以及高效安全充電方面進行了分析研究。結果表明，在高速度和資源豐富的FPGA(現(xiàn)場可編程門陣列)支持下，模糊控制的雙軸追蹤系統(tǒng)能在天氣晴朗的情況下，使太陽光與電池板垂直偏差不超過3度；在粒子群算法調(diào)節(jié)下的電池板，在1秒內(nèi)能跟蹤到最大功率值點；采用階段恒流快速充電的智能充電系統(tǒng)，能將65AH的蓄電池充電時間縮短到1-2小時。
[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.
【學位授予單位】：重慶理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM914.4;TM912
本文編號：2168884
[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.
【學位授予單位】：重慶理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM914.4;TM912
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