光伏组件爬坡与人工神经网络最大功率点跟踪技术的比较

Zarrad Ons, J. Aymen, A. Craciunescu, M. Popescu
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引用次数: 10

摘要

对光伏发电系统中两种最大功率点跟踪算法进行了分析和比较。利用Matlab/Simulink建立了具有MPPT功能的光伏系统模型。该系统是将太阳能组件模型和DC-DC升压变换器模型分别与爬坡算法(HC)和人工神经网络算法(ANC)相结合而开发的。在不同的大气条件和MPPT算法下对系统进行了仿真。通过仿真结果的比较,可以得出采用ANN MPPT算法的光伏系统更简单,不需要了解系统内部参数,计算量少,速度更快,为多变量问题提供了紧凑的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of Hill-Climbing and Artificial Neural Network Maximum Power Point Tracking Techniques for Photovoltaic Modules
In this paper, two maximum power point tracking (MPPT) algorithms in a photovoltaic electrical energy generation system are analyzed and compared. The Matlab/Simulink is used to establish the model of a photovoltaic system with MPPT function. This system is developed by combining the models of established solar module and DC-DC boost converter with the algorithms of hill climbing (HC) and artificial neural network (ANC), respectively. The system is simulated under different atmospheric conditions and MPPT algorithms. According to the comparisons among the simulation results, it can be concluded that the photovoltaic system with ANN MPPT algorithm is simpler: it does not require knowledge of internal system parameters, needs less calculation, is faster and provides a compact solution for multi-variable problems.
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