Krishnaram K, Suresh Padmanabhan T, Alsaif Faisal, Senthilkumar S
Department of EEE, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, India.
Department of Electrical Engineering, College of Engineering, King Saud University, 11421, Riyadh, Saudi Arabia.
Sci Rep. 2024 Apr 22;14(1):9256. doi: 10.1038/s41598-024-59900-z.
The conventional MPPT method has drawbacks, such as that under partial shading conditions, several peaks occur and identifying the global peak is difficult. It may converge to a local peak and lead to poor conversion efficiency and tracking efficiency. Implementation of a hybrid algorithm by integrating P&O and metaheuristic algorithms can perform better under partial shading conditions. But the tracking speed is low and the response time is longer. To mitigate the issues mentioned above, a new hybrid algorithm has been suggested that integrates GWO and a modified fast terminal sliding mode controller (MFTSMC). The suggested method with three phase ILBC is incorporated into the PV system. The MATLAB tool is employed to experiment with this study. The performance of GWO-MFTSMC is analysed through MATLAB/ SIMULINK and compared with the performance of ANN-FTSMC and PSO-FTSMC algorithm based MPPT techniques. A hardware prototype is developed and tested for 5 × 200 W solar PV modules with the GWO-MFTSMC algorithm. The proposed method conversion efficiency is 99.72% and 96.15% under simulation and hardware realisation, respectively, which is higher than the ANN-FTSMC and PSO-FTSMC methods.
传统的最大功率点跟踪（MPPT）方法存在缺点，例如在部分阴影条件下会出现多个峰值，难以识别全局峰值。它可能会收敛到局部峰值，导致转换效率和跟踪效率低下。通过集成扰动观察法（P&O）和元启发式算法实现的混合算法在部分阴影条件下可以表现得更好。但跟踪速度较低，响应时间较长。为了缓解上述问题，有人提出了一种新的混合算法，该算法集成了灰狼优化算法（GWO）和改进的快速终端滑模控制器（MFTSMC）。所提出的带有三相交错升压变换器（ILBC）的方法被应用于光伏系统。本研究使用MATLAB工具进行实验。通过MATLAB/SIMULINK分析了GWO-MFTSMC的性能，并与基于人工神经网络-快速终端滑模控制器（ANN-FTSMC）和粒子群优化-快速终端滑模控制器（PSO-FTSMC）算法的MPPT技术的性能进行了比较。开发了一个硬件原型，并使用GWO-MFTSMC算法对5×200W的太阳能光伏模块进行了测试。所提出的方法在仿真和硬件实现下的转换效率分别为99.72%和96.15%，高于ANN-FTSMC和PSO-FTSMC方法。
