Bouteraa Yassine, Khishe Mohammad
Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia.
Innovation Center for Artificial Intelligence Applications, Yuan Ze University, Taoyuan City, Taiwan.
Sci Rep. 2024 Nov 26;14(1):29261. doi: 10.1038/s41598-024-80849-6.
The dynamic and multimodal nature of photovoltaic (PV) systems makes it challenging to examine all solar photovoltaic characteristics. Consequently, this study recommends a recently developed optimization method called the marine predator algorithm (MPA) for developing reliable PV models. In the traditional MPA, the two main search processes are Lévy flight (LF) and Brownian walk (BW), and the switch across them is unpredictable. This is while the transition between these two mechanisms is naturally continuous and dynamic. To rectify the limitation mentioned above, this research paper presents an innovative, dynamic shift function that effectively modulates the interplay that exists between the BW and LF procedures. By enhancing the changeover pattern between the primary phases of MPA, the suggested dynamic walk substantially boosts the performance of MPA. The dynamic Lévy-Brownian MPA (DLBMPA) is also made to be resilient in dealing with the parameterization limitations of PV Modeling approaches by using a constraint handling technique. The performance of DLBMPA is tested using ten popular optimization methods. Employing the DLBMPA achieved an average RMSE of 9.7 × 10 in the parameter estimation across a number of multiple PV models, including the SDM, DDM, and TDM, where out of the ten optimization algorithms experimented, this was statistically significant (p < 0.05) better. In terms of averaged computation time, DLBMPA was 13 ms and still showed high accuracy in dealing with different irradiance and temperature levels. These improvements allow for MBPA to be credited as having a high efficiency when estimating the PV parameters since its speed of convergence and accuracy level surpass the previous techniques used.
光伏（PV）系统的动态和多模态特性使得全面研究所有太阳能光伏特性具有挑战性。因此，本研究推荐一种最近开发的优化方法——海洋捕食者算法（MPA）来建立可靠的光伏模型。在传统的MPA中，两个主要搜索过程是莱维飞行（LF）和布朗运动（BW），它们之间的切换是不可预测的。然而，这两种机制之间的转换实际上是自然连续且动态的。为了纠正上述局限性，本文提出了一种创新的动态切换函数，该函数有效地调节了BW和LF过程之间的相互作用。通过增强MPA主要阶段之间的转换模式，所提出的动态游走显著提高了MPA的性能。通过使用约束处理技术，动态莱维 - 布朗MPA（DLBMPA）还能够有效应对光伏建模方法的参数化限制。使用十种流行的优化方法对DLBMPA的性能进行了测试。在包括SDM、DDM和TDM在内的多个光伏模型的参数估计中，采用DLBMPA实现了平均均方根误差为9.7×10，在实验的十种优化算法中，这在统计上具有显著优势（p < 0.05）。在平均计算时间方面，DLBMPA为13毫秒，并且在处理不同辐照度和温度水平时仍显示出高精度。这些改进使得MBPA在估计光伏参数时具有高效率，因为其收敛速度和精度水平超过了以前使用的技术。
