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
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 a...
- Autores:
-
Robles Algarín, Carlos
Restrepo-Leal, Diego
Ospino C., Adalberto
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7476
- Acceso en línea:
- https://hdl.handle.net/11323/7476
https://doi.org/10.1016/j.dib.2019.104669
https://repositorio.cuc.edu.co/
- Palabra clave:
- Artificial neural networks
Multimodal functions
Optimization algorithms
Partial shading
Photovoltaic modules
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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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 |
| 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 |
| spellingShingle |
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 Artificial neural networks Multimodal functions Optimization algorithms Partial shading Photovoltaic modules |
| title_short |
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 |
| title_full |
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 |
| title_fullStr |
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 |
| title_full_unstemmed |
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 |
| title_sort |
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.creator.fl_str_mv |
Robles Algarín, Carlos Restrepo-Leal, Diego Ospino C., Adalberto |
| dc.contributor.author.spa.fl_str_mv |
Robles Algarín, Carlos Restrepo-Leal, Diego Ospino C., Adalberto |
| dc.subject.spa.fl_str_mv |
Artificial neural networks Multimodal functions Optimization algorithms Partial shading Photovoltaic modules |
| topic |
Artificial neural networks Multimodal functions Optimization algorithms Partial shading Photovoltaic modules |
| 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. |
| publishDate |
2019 |
| dc.date.issued.none.fl_str_mv |
2019 |
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2020-11-24T16:33:44Z |
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2020-11-24T16:33:44Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/resource_type/c_6501 |
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Text |
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info:eu-repo/semantics/article |
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http://purl.org/redcol/resource_type/ART |
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info:eu-repo/semantics/acceptedVersion |
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https://doi.org/10.1016/j.dib.2019.104669 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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https://hdl.handle.net/11323/7476 https://doi.org/10.1016/j.dib.2019.104669 https://repositorio.cuc.edu.co/ |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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eng |
| dc.relation.references.spa.fl_str_mv |
[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. [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. [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. [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. [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. [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. [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. [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. [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. |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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Robles Algarín, Carlos9e951190a3565d3f527ba6c861b118a4Restrepo-Leal, Diego860fc17a20723280f5facedbc6a2f3efOspino C., Adalbertodc979344fb023fb42436d7b9e8e53d192020-11-24T16:33:44Z2020-11-24T16:33:44Z2019https://hdl.handle.net/11323/7476https://doi.org/10.1016/j.dib.2019.104669Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Data in briefhttps://www.sciencedirect.com/science/article/pii/S2352340919310248?via%3DihubArtificial neural networksMultimodal functionsOptimization algorithmsPartial shadingPhotovoltaic modulesData from multimodal functions based on an array of photovoltaic modules and an approximation with artificial neural networks as a scenario for testing optimization algorithmsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[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.[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.[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.[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.[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.[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.[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.[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.[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.ORIGINALData from multimodal functions based on an array of photovoltaic modules and an approximation with artificial neural networks as a scenario for testing optimization algorithms.pdfData from multimodal functions based on an array of photovoltaic modules and an approximation with artificial neural networks as a scenario for testing optimization algorithms.pdfapplication/pdf725472https://repositorio.cuc.edu.co/bitstream/11323/7476/1/Data%20from%20multimodal%20functions%20based%20on%20an%20array%20of%20photovoltaic%20modules%20and%20an%20approximation%20with%20artificial%20neural%20networks%20as%20a%20scenario%20for%20testing%20optimization%20algorithms.pdfb0efd79af423659daea5ae1be62d60d8MD51open accessCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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algorithms.pdf.jpgimage/jpeg42050https://repositorio.cuc.edu.co/bitstream/11323/7476/4/Data%20from%20multimodal%20functions%20based%20on%20an%20array%20of%20photovoltaic%20modules%20and%20an%20approximation%20with%20artificial%20neural%20networks%20as%20a%20scenario%20for%20testing%20optimization%20algorithms.pdf.jpg3f1f080bc8344e158e5a9f6e84762714MD54open accessTEXTData from multimodal functions based on an array of photovoltaic modules and an approximation with artificial neural networks as a scenario for testing optimization algorithms.pdf.txtData from multimodal functions based on an array of photovoltaic modules and an approximation with artificial neural networks as a scenario for testing optimization algorithms.pdf.txttext/plain14905https://repositorio.cuc.edu.co/bitstream/11323/7476/5/Data%20from%20multimodal%20functions%20based%20on%20an%20array%20of%20photovoltaic%20modules%20and%20an%20approximation%20with%20artificial%20neural%20networks%20as%20a%20scenario%20for%20testing%20optimization%20algorithms.pdf.txt59f8110e61b64db00ba823e6b2dbf1deMD55open access11323/7476oai:repositorio.cuc.edu.co:11323/74762023-12-14 14:59:26.953Attribution-NonCommercial-NoDerivatives 4.0 International|||http://creativecommons.org/licenses/by-nc-nd/4.0/open accessRepositorio Universidad de La 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