Robles-Algarín Carlos, Restrepo-Leal Diego, Ospino Castro Adalberto
Universidad del Magdalena, Facultad de Ingeniería, Carrera 32 No 22 - 08, Santa Marta, Colombia.
Universidad de la Costa, Facultad de Ingeniería, Calle 58 No 55-66, Barranquilla, Colombia.
Data Brief. 2019 Oct 16;27:104669. doi: 10.1016/j.dib.2019.104669. eCollection 2019 Dec.
This paper presents the data of multimodal functions that emulate the performance of an array of five photovoltaic modules under partial shading conditions. These functions were obtained through mathematical modeling and represent the P-V curves of a photovoltaic module with several local maximums and a global maximum. In addition, data from a feedforward neural network are shown, which represent an approximation of the multimodal functions that were obtained with mathematical modeling. The modeling of multimodal functions, the architecture of the neural network and the use of the data were discussed in our previous work entitled "Search for Global Maxima in Multimodal Functions by Applying Numerical Optimization Algorithms: A Comparison Between Golden Section and Simulated Annealing" [1]. Data were obtained through simulations in a C code, which were exported to DAT files and subsequently organized into four Excel tables. Each table shows the voltage and power data for the five modules of the photovoltaic array, for multimodal functions and for the approximation of the multimodal functions implemented by the artificial neural network. In this way, a dataset that can be used to evaluate the performance of optimization algorithms and system identification techniques applied in multimodal functions was obtained.
本文展示了在部分阴影条件下模拟五个光伏模块阵列性能的多模态函数数据。这些函数通过数学建模获得，代表了具有多个局部最大值和一个全局最大值的光伏模块的P-V曲线。此外，还展示了来自前馈神经网络的数据，这些数据代表了通过数学建模获得的多模态函数的近似值。多模态函数的建模、神经网络的架构以及数据的使用在我们之前的题为《应用数值优化算法在多模态函数中寻找全局最大值：黄金分割法与模拟退火法的比较》[1]的工作中进行了讨论。数据通过C代码模拟获得，导出到DAT文件，随后整理成四个Excel表格。每个表格展示了光伏阵列五个模块的电压和功率数据、多模态函数以及人工神经网络实现的多模态函数近似值的数据。通过这种方式，获得了一个可用于评估应用于多模态函数的优化算法和系统识别技术性能的数据集。
