摘要
由于常规能源逐渐面临枯竭以及日本大地震带来的核安全危机,清洁的可再生的太阳能越来越受到人们的重视,并且在未来光伏电池的应用中有着良好的发展前景。但由于光伏电池的转换效率较低,且价格较高,严重阻碍了光伏系统的推广和应用,因此必须最大限度的利用光伏电池所产生的功率,以降低光伏发电的成本。因此本文研究的重点是使光伏组件工作在最大功率点处,以获得最大的输出功率。
本论文研究了光伏电池的结构和原理,深入探讨了光伏系统的组成,特别是在非均匀光照条件下,系统的输出特性,针对光伏系统的非线性特性,将BP神经网络进行优化,提出了基于遗传神经网络的多峰最大功率跟踪方案,以提高跟踪的速度和精度。本文还重点研究和建立了基于Proteus的适用于光照不均匀情况下的光伏组件多峰仿真模型以及硬件跟踪系统。主要进行了以下四方面的研究:
(1)分析了在光照不均匀条件下,光伏组件的特性,得出组件的I-V方程和P-V方程,并在MATLAB环境下,仿真出它的输出特性曲线,为后续的最大功率的跟踪打下基础。
(2)深入分析了BP神经网络的基本工作原理,结合本系统的特点,将BP神经网络应用于多峰最大功率的追踪中,并且分析了其追踪的效果。
(3)针对BP算法的不足,提出了遗传算法优化神经网络(GABP)的方案。实验证明,采用此方案对多峰最大功率进行跟踪,可以充分结合遗传算法的全局搜索能力和BP算法的局部搜索能力,加快收敛速度(迭代次数从122降到10),提高了追踪的精度(平均误差S_N从0.156降到0.0021)。
(4)研究和建立基于Proteus的适用于光照不均匀情况下的光伏组件的多峰仿真模型及硬件跟踪系统,给出了光伏组件中含有旁路二极管时的仿真模型。为系统的硬件研究打下了基础。
As the result of the lacking traditional resources (such as coal, oil natural gas, etc.) and the nuclear security crisis cased by Japanese earthquake, people begin to pay more and more their attentions to clean, renewable solar energy. So in the future, the applications of photovoltaic cells have good development of prospects. However, a low conversion efficiency of photovoltaic cells, and the higher prices have been a serious obstacle to the promotion and application of PV systems. There is a method to maximize using the power generated by PV cells: making PV cells output maximum power to reduce the circuit loss. So in this article the method of maximum power point tracking makes the PV cells working at the maximum power point to obtain the maximum output power, which will be focused research.
This paper studied the structure and principle of photovoltaic battery, deeply discussed the composition of the photovoltaic system and the characteristics of system’s outputs especially in non-uniform lighting conditions. Because of the nonlinear characteristics of the photovoltaic system, the neural network based on the back propagation (BP) neural network was applied in the tracking the maximum power point. The BP algorithm is the local search method based on the gradient descent, to overcome the local search character,the genetic algorithm was applied to optimize the BP neural network. This paper also established the milt-peak simulation model based on Proteus software.
As for the research of the characteristics of PV modules in the non-uniform conditions, it is concluded the component I-V equation and P-V equation. And the simulation shows its output’s characteristic curves, these studies lay the foundation of the tracing the maximum power.
As for the research of the basic principle of the BP neural network and the system’s characteristics and the neural network based on the back propagation (BP) neural network was applied in tracking the maximum power point.
Because of the shortage of the BP algorithm, the genetic algorithm is put forward to optimize the neural network weigh coefficient. The experiment shows that the genetic algorithm optimizing neural network can combine with the global search character of genetic algorithm and local search character of BP algorithm. The genetic neural network can accelerate the speed of convergence (the iterative number descended form 122 to 10) and improve the accuracy(the average error descended from 0.156 to0.0021 respectively).
The multi-summit PV module basing on the Proteus software is established in the non-illumination lighting conditions. Basing on the multi-summit model, we can design a multi-summit MPPT algorithm that can work under uniform isolation and non-uniform isolation.
引文
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