Department of Electrical and Electronic Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32951, Egypt.
Department of Electrical Engineering, College of Engineering, Shaqra University, 75311, Dawadmi, Saudi Arabia.
Sci Rep. 2023 Mar 14;13(1):4189. doi: 10.1038/s41598-023-28982-6.
Recent research has focused on photovoltaic (PV) systems due to their important properties. The efficiency of the PV system can be enhanced by many Maximum Power Point Tracking (MPPT) algorithms proposals. MPPT algorithms are used to achieve maximum PV output power by optimizing the duty cycle of the DC-DC buck/boost converter. This paper introduces an RNA algorithm as an efficient MPPT algorithm for the photovoltaic system. This proposed RNA algorithm consists of two main segments. The first segment is an artificial neural network for generating reference power. The second segment is a proposed Recursive Bit Assignment (RBA) network to allow variable step size of the boost converter duty cycle. The instant PV power adopts the RBA network to produce the variable duty cycle increment value. Additionally, the neural network is implemented in such a way to obtain the best performance. Many simulation results using MATLAB to test the system performance are presented. The performance characteristics of the photovoltaic system with variable irradiance and variable temperature are simulated. From results, the proposed RNA algorithm achieves fast tracking time, high energy efficiency, true maximum power point and acceptable ripple. Additionally, comparisons between the RNA algorithm and other related algorithms such as Perturb and Observe, the Neural Network and the Adaptive Neural Inference System Algorithms are executed. The proposed RNA algorithm achieves the best performance in all case studies such as; irradiance profile variation, severe temperature and irradiance diversions, and partial shading conditions. Besides, the experimental circuit of the PV system is also presented.
最近的研究集中在光伏 (PV) 系统上，因为它们具有重要的特性。可以通过许多最大功率点跟踪 (MPPT) 算法提案来提高 PV 系统的效率。MPPT 算法用于通过优化 DC-DC 降压/升压转换器的占空比来实现最大 PV 输出功率。本文介绍了一种 RNA 算法，作为光伏系统的高效 MPPT 算法。该提出的 RNA 算法由两个主要部分组成。第一部分是用于生成参考功率的人工神经网络。第二部分是提出的递归位分配 (RBA) 网络，以允许升压转换器占空比的可变步长。即时 PV 功率采用 RBA 网络来产生可变占空比增量值。此外，神经网络以获得最佳性能的方式实现。使用 MATLAB 呈现了许多测试系统性能的仿真结果。模拟了具有可变辐照度和可变温度的光伏系统的性能特征。从结果来看，所提出的 RNA 算法实现了快速跟踪时间、高能量效率、真实最大功率点和可接受的纹波。此外，还执行了 RNA 算法与其他相关算法（如扰动量观察、神经网络和自适应神经网络推理系统算法）之间的比较。在所研究的所有案例中，如辐照度分布变化、严重的温度和辐照度偏差以及部分阴影条件下，所提出的 RNA 算法都能实现最佳性能。此外，还提出了 PV 系统的实验电路。
