| dc.creator | Robles Algarín, Carlos | |
| dc.creator | Restrepo-Leal, Diego | |
| dc.creator | Ospino C., Adalberto | |
| dc.date | 2020-11-24T16:33:44Z | |
| dc.date | 2020-11-24T16:33:44Z | |
| dc.date | 2019 | |
| dc.date.accessioned | 2023-10-03T19:32:39Z | |
| dc.date.available | 2023-10-03T19:32:39Z | |
| dc.identifier | https://hdl.handle.net/11323/7476 | |
| dc.identifier | https://doi.org/10.1016/j.dib.2019.104669 | |
| dc.identifier | Corporación Universidad de la Costa | |
| dc.identifier | REDICUC - Repositorio CUC | |
| dc.identifier | https://repositorio.cuc.edu.co/ | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/9170565 | |
| dc.description | 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. | |
| dc.format | application/pdf | |
| dc.format | application/pdf | |
| dc.language | eng | |
| dc.publisher | Corporación Universidad de la Costa | |
| dc.relation | [1] J. Guillot, D. Restrepo Leal, C. Robles Algarín, I. Oliveros, Search for global Maxima in multimodal functions by applying
numerical optimization algorithms: a Comparison between golden section and simulated annealing, Computation 7 (3)
(2019) 43, https://doi.org/10.3390/computation7030043. | |
| dc.relation | [2] C.R. Algarín, D.S. Hernández, D.R. Leal, A low-cost maximum power point tracking system based on neural network inverse model controller, Electronics 7 (1) (2018) 4, https://doi.org/10.3390/electronics7010004. | |
| dc.relation | [3] J. Guerrero, Y. Munoz, F. Ib ~ a nez, A. Ospino, Analysis of mismatch and shading effects in a photovoltaic array using different ~
technologies, IOP Conf. Ser.-Mat. Sci. 59 (1) (2014) 012007, https://doi.org/10.1088/1757-899X/59/1/012007. | |
| dc.relation | [4] W.D. Chang, Multimodal function optimizations with multiple maximums and multiple minimums using an improved PSO
algorithm, Appl. Soft Comput. 60 (2017) 60e72, https://doi.org/10.1016/j.asoc.2017.06.039. | |
| dc.relation | [5] V. Kaczmarczyk, Z. Bradac, P. Fiedler, A heuristic algorithm to compute multimodal criterial function weights for demand
management in residential areas, Energies 10 (7) (2017) 1049, https://doi.org/10.3390/en10071049. | |
| dc.relation | [6] J. Viloria Porto, C. Robles Algarín, D. Restrepo Leal, A novel approach for an MPPT controller based on the ADALINE network
trained with the RTRL algorithm, Energies 11 (12) (2018) 3407, https://doi.org/10.3390/en11123407. | |
| dc.relation | [7] H.M.H. Farh, A.M. Eltamaly, A.B. Ibrahim, M.F. Othman, M.S. Al-Saud, Dynamic global power extraction from partially
shaded photovoltaic using deep recurrent neural network and improved PSO techniques, Int. T. Electr. Energy 9 (1) (2019),
e12061, https://doi.org/10.1002/2050-7038.12061. | |
| dc.relation | [8] M. Zhang, Z. Chen, L. Wei, An immune firefly algorithm for tracking the maximum power point of PV array under partial
shading conditions, Energies 12 (16) (2019) 3083, https://doi.org/10.3390/en12163083. | |
| dc.relation | [9] R.S. Kulkarni, D.B. Talange, Modeling of solar photovoltaic module using system identification, in: Proceedings of International Conference on Power Systems, India, 2018, pp. 782e784, https://doi.org/10.1109/ICPES.2017.8387395. | |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.source | Data in brief | |
| dc.source | https://www.sciencedirect.com/science/article/pii/S2352340919310248?via%3Dihub | |
| dc.subject | Artificial neural networks | |
| dc.subject | Multimodal functions | |
| dc.subject | Optimization algorithms | |
| dc.subject | Partial shading | |
| dc.subject | Photovoltaic modules | |
| dc.title | Data from multimodal functions based on an array of photovoltaic modules and an approximation with artificial neural networks as a scenario for testing optimization algorithms | |
| dc.type | Artículo de revista | |
| dc.type | http://purl.org/coar/resource_type/c_6501 | |
| dc.type | Text | |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion | |
| dc.type | http://purl.org/redcol/resource_type/ART | |
| dc.type | info:eu-repo/semantics/acceptedVersion | |
| dc.type | http://purl.org/coar/version/c_ab4af688f83e57aa | |
