基于果蝇优化算法的多峰值光伏最大功率跟踪控制策略研究

发布时间：2018-07-23 19:21

【摘要】：在光伏发电系统实际工程应用中,光伏阵列的表面很容易出现局部遮挡现象。当光伏阵列处于局部阴影遮挡时,不同阴影遮挡方式下输出特性曲线规律不同,传统的跟踪方法容易陷入局部最大值,因此本文具体研究了光伏阵列在不同阴影遮挡情况下的最大功率点跟踪(MPPT)结果。本文对以下几个方面进行了重点研究。(1)通过分析光伏电池的物理工作原理,建立光伏阵列的数学模型和工程模型,在此基础上推导出适用于局部阴影下的数学模型。在Matlab/Simulink建立了光伏阵列仿真模型,得到不同遮挡模式下光伏阵列的U-I和U-P输出特性曲线,在此基础上总结了U-P多峰值曲线的分布规律,并提出光伏阵列在安装和设计时应该考虑的问题。(2)分析并对比了光伏阵列在不同情况的几种MPPT控制方法。本文详细地介绍了果蝇算法的原理、目前应用领域和算法步骤,在该算法基础上进行改进优化,经过测试函数验证,改进的果蝇优化算法寻优性能更好,全局收敛速度更快。(3)在Matlab/Simulink中搭建了5.5KW光伏发电系统模型,分析了在正常光照、不同局部遮挡模式情况下光伏阵列的输出峰值情况,懫用粒子群算法与改进的果蝇优化算法进行跟踪对比,验证了改进的果蝇优化算法具有良好的收敛性和准确性,得出了改进的果蝇算法能够有效地提高多峰值的光伏阵列输出功率结论。
[Abstract]:In the practical application of photovoltaic system, the surface of photovoltaic array is prone to local occlusion. When photovoltaic array is in local shadow occlusion, the law of output characteristic curve is different under different shadow occlusion mode, and the traditional tracking method is easy to fall into local maximum value. Therefore, the maximum power point tracking (MPPT) results of photovoltaic arrays under different shading conditions are studied in detail. This paper focuses on the following aspects: (1) by analyzing the physical working principle of photovoltaic cells, the mathematical model and engineering model of photovoltaic array are established, and the mathematical model suitable for local shadow is derived. The simulation model of photovoltaic array is established in Matlab/Simulink, and the U-I and U-P output characteristic curves of photovoltaic array under different occlusion modes are obtained. On this basis, the distribution law of U-P multi-peak curve is summarized. The problems that should be considered in the installation and design of photovoltaic arrays are put forward. (2) several MPPT control methods for photovoltaic arrays in different cases are analyzed and compared. In this paper, the principle of Drosophila algorithm is introduced in detail. At present, the application fields and steps of the algorithm are introduced, and the improved algorithm is optimized on the basis of this algorithm. The global convergence speed is faster. (3) the 5.5KW photovoltaic system model is built in Matlab/Simulink, and the output peak value of photovoltaic array under normal illumination and different local occlusion modes is analyzed. The particle swarm optimization algorithm is compared with the improved Drosophila optimization algorithm, and the improved algorithm has good convergence and accuracy. It is concluded that the improved Drosophila algorithm can effectively increase the output power of multi-peak photovoltaic arrays.
【学位授予单位】：上海电机学院
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TM615

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