Krishnaram K, Sivamani S, Alaas Zuhair, Ahmed M M R, Senthilkumar S, Raj S Antony
Department of Electrical and Electronics Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, 611002, India.
Department of Electrical Engineering, Faculty of Engineering, Jazan University, 45142, Jizan, Saudi Arabia.
Sci Rep. 2024 May 14;14(1):10984. doi: 10.1038/s41598-024-61856-z.
Photovoltaic (PV)-based power generation systems are becoming increasingly popular as a due to its high performance and cleanliness. Several factors influence the performance of a PV system, including shadowing effects. PV systems employ MPPT methodologies to obtain the power from PV array. Conventional MPPTs works well under normal conditions when there is no shadow effects or partial shading. The presence of partial shading affects the system performance and generates several power peaks. This complicates the process of finding out of the global peak (GMPP) with improved tracking efficiency and reduced settling time including conversion efficiency. This work proposes three hybrid MPPT techniques: Water Cycle Optimisation-Perturb and Observe (WCO-PO), Artificial Neural Network Supported Adaptable Stepped-Scaled Perturb and Observe (ANN-ASSPO), Grey Wolf Optimisation-Modified Fast Terminal Sliding Mode Controller (GWO-MFTSMC), and two conventional MPPT techniques WCO and P&O have been implemented. The proposed system utilizes interleaved boost converter with three phase. The performances of proposed hybrid MPPTs strategies were compared in terms of output voltage, output current and extracted power. The comparison also includes conversion efficiency and average settling time. To analyse the performances, four different cases have been used to test the efficacy of hybrid MPPTs under changing climatic conditions. The MATLAB/Simulink tool has been used to analyze the PV system performances. In the three hybrid MPPT techniques, WCO-PO has performed better when compared to other two hybrid MPPTs in terms of conversion efficiency (99.56%) and settling time (1.4 m).
基于光伏（PV）的发电系统因其高性能和清洁性而越来越受欢迎。有几个因素会影响光伏系统的性能，包括阴影效应。光伏系统采用最大功率点跟踪（MPPT）方法从光伏阵列获取功率。传统的MPPT在没有阴影效应或部分遮挡的正常条件下工作良好。部分遮挡的存在会影响系统性能并产生多个功率峰值。这使得找到全局峰值（GMPP）的过程变得复杂，需要提高跟踪效率并减少包括转换效率在内的稳定时间。这项工作提出了三种混合MPPT技术：水循环优化-扰动观察（WCO-PO）、人工神经网络支持的自适应阶梯式扰动观察（ANN-ASSPO）、灰狼优化-改进的快速终端滑模控制器（GWO-MFTSMC），并且已经实现了两种传统的MPPT技术WCO和P&O。所提出的系统采用三相交错升压变换器。从输出电压、输出电流和提取功率方面比较了所提出的混合MPPT策略的性能。比较还包括转换效率和平均稳定时间。为了分析性能，使用了四种不同的情况来测试混合MPPT在变化气候条件下的有效性。使用MATLAB/Simulink工具来分析光伏系统的性能。在这三种混合MPPT技术中，WCO-PO在转换效率（99.56%）和稳定时间（1.4毫秒）方面比其他两种混合MPPT表现更好。
