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Experimental analysis of genetic algorithms based MPPT for PV systems

机译:基于遗传算法MPPT的光伏系统实验分析

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This paper presents experimental analysis of Genetic Algorithms (GAs) based Maximum Power Point Tracking (MPPT) for photovoltaic (PV) systems. This method, presented by another paper [1], uses GAs to track the maximum power point (MPP) of PV panels. Comparison with the famous Perturb and Observe (P&O) and Incremental Conductance (Inc-Cond) are given, we tested stability (power oscillation) with real panels (Conergy PowerPlus 214P), to compare response time (rapidity) we used a PV emulator [2] so we can inject the same irradiance profile and see output PV power evolution. The response time, of P&O and Inc-Cond, and the PV power oscillation varies with the duty cycle increment step; with a small step, we get less power oscillation but this needs an important time response, we can improve system rapidity with a bigger duty increment step but important power oscillation will result. With GAs based MPPT we can get more stability with rapid response time. The results obtained show better stability and less oscillation around the MPP with the new method.
机译:本文介绍了基于遗传算法(气体)基于光伏(PV)系统的最大功率点跟踪(MPPT)的实验分析。通过另一种纸张[1]呈现的该方法使用气体跟踪PV面板的最大功率点(MPP)。给出了着名的扰动和观察(P&O)和增量电导(Inc-Cond)的比较,我们用真正的面板(Conergy PowerPlus 214P)测试了稳定性(电力振荡),以比较响应时间(快速)我们使用PV仿真器[ 2]所以我们可以注入相同的辐照概况并查看输出光伏电源演进。 P&O和Inc-Cond的响应时间和光伏电源振荡随占空比增量步骤而变化;通过一小步,我们得到了更少的功率振荡,但这需要一个重要的时间响应,我们可以通过更大的职责增量步骤提高系统速度,但重要的功率振荡将会导致重要的功率振荡。通过基于气体的MPPT,我们可以快速响应时间获得更多的稳定性。通过新方法,获得的结果显示出更好的稳定性和较少的振荡围绕MPP。

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