Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review.
Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review.
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DOI：
10.3390/mi12101260
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发表时间：
2021-10-17
期刊：
影响因子：
3.4
通讯作者：
Rodríguez-Abreo O
中科院分区：
文献类型：
--
作者：
Villegas-Mier CG;Rodriguez-Resendiz J;Álvarez-Alvarado JM;Rodriguez-Resendiz H;Herrera-Navarro AM;Rodríguez-Abreo O
用于清洁电能的光伏系统的使用已经增加。然而，由于其效率低，研究人员一直在寻找提高其有效性和效率的方法。最大功率点跟踪（MPPT）逆变器使我们能够最大限度地从光伏面板中提取尽可能多的能量，并且它们需要算法来提取最大功率点（MPP）。一些智能算法表现出可接受的性能，但是，很少有人考虑使用人工神经网络（ANN）。这些具有给出MPP的快速且准确的跟踪的优点。控制器的有效性取决于隐藏层中使用的算法以及神经网络的训练程度。研究了过去六年的文章。不同的论文，报告和其他文件使用人工神经网络的MPPT控制的审查。该算法是基于人工神经网络或在一个混合组合与FL或元启发式算法。根据这项研究，ANN MPPT算法在均匀条件下的平均性能为98%，具有更快的收敛速度，并且在MPP周围的振荡更少。
The use of photovoltaic systems for clean electrical energy has increased. However, due to their low efficiency, researchers have looked for ways to increase their effectiveness and improve their efficiency. The Maximum Power Point Tracking (MPPT) inverters allow us to maximize the extraction of as much energy as possible from PV panels, and they require algorithms to extract the Maximum Power Point (MPP). Several intelligent algorithms show acceptable performance; however, few consider using Artificial Neural Networks (ANN). These have the advantage of giving a fast and accurate tracking of the MPP. The controller effectiveness depends on the algorithm used in the hidden layer and how well the neural network has been trained. Articles over the last six years were studied. A review of different papers, reports, and other documents using ANN for MPPT control is presented. The algorithms are based on ANN or in a hybrid combination with FL or a metaheuristic algorithm. ANN MPPT algorithms deliver an average performance of 98% in uniform conditions, exhibit a faster convergence speed, and have fewer oscillations around the MPP, according to this research.
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作者：
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