本文選題：復(fù)雜應(yīng)用環(huán)境 + 光伏最大功率點跟蹤��； 參考：《南昌大學》2017年碩士論文

【摘要】：群體智能算法在解決具有多局部極值點的優(yōu)化問題時,具有十分明顯的優(yōu)勢,因此被廣泛的使用,但由于算法本身存在一些弊端,也使得群體智能算法的應(yīng)用受到了局限。本文針對群體智能算法普遍具有的“早熟”弊端,提出了一種混沌改進群體智能算法,并以人工蜂群算法和貓群算法為例,根據(jù)算法的原有數(shù)學模型提供了改進說明。仿真以光伏為背景,搭建符合實際復(fù)雜情況下的光伏系統(tǒng)模型,將改進后的算法應(yīng)用在局部陰影情況下的光伏最大功率點跟蹤,并分別進行了靜態(tài)特性分析和動態(tài)特性分析。仿真結(jié)果很好地說明了所提算法能夠解決原算法的“早熟”問題,具有較高的精度和更快的收斂速度,且算法的魯棒性較好。最后通過將改進后的人工蜂群算法以及改進后的貓群算法與粒子群算法進行對比,驗證了所提算法的優(yōu)越性。為進一步證實算法的有效性和高效性,搭建了光伏系統(tǒng)小功率實驗平臺,編寫了改進前后算法的程序,得到實驗當天跟蹤到的最大功率及其對應(yīng)的輸出電壓、電流。實驗結(jié)果很好地說明了改進前后的算法均能根據(jù)光照強度的改變快速跟蹤到新的功率點,但本文所提算法的跟蹤精度更高,獲得的平均功率值更大。
[Abstract]:Swarm intelligence algorithm has obvious advantages in solving the optimization problem with multi-local extremum, so it is widely used. However, due to some shortcomings of the algorithm itself, the application of swarm intelligence algorithm is also limited. In this paper, a chaotic improved swarm intelligence algorithm is proposed to overcome the "premature" disadvantage of swarm intelligence algorithm. Taking artificial bee colony algorithm and cat swarm algorithm as examples, an improved explanation is provided according to the original mathematical model of the algorithm. The simulation takes photovoltaic as the background, sets up the photovoltaic system model in accordance with the actual complex situation, applies the improved algorithm to the photovoltaic maximum power point tracking under the local shadow, and carries on the static characteristic analysis and the dynamic characteristic analysis respectively. The simulation results show that the proposed algorithm can solve the "premature" problem of the original algorithm, has higher accuracy and faster convergence speed, and has better robustness. Finally, by comparing the improved artificial bee swarm algorithm and the improved cat swarm optimization algorithm with the particle swarm optimization algorithm, the superiority of the proposed algorithm is verified. In order to further verify the effectiveness and efficiency of the algorithm, a small power experimental platform of photovoltaic system was built, and the program of the improved algorithm was compiled. The maximum power and the corresponding output voltage and current were obtained on the day of the experiment. The experimental results show that the improved algorithm can quickly track the new power points according to the change of illumination intensity, but the tracking accuracy of the proposed algorithm is higher and the average power value is larger.
【學位授予單位】：南昌大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM615

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本文編號：2117929