SOLAR COMET PROJECT - CS FOR PHOTOVOLTAIC SYSTEM OPTIMIZATION
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Keywords

Renewable Energies
Bio-Inspired Neural Training
Solar Heliostatic Comet
MPPT Optimization.

How to Cite

Sandoval-Ruiz, C. E. (2020). SOLAR COMET PROJECT - CS FOR PHOTOVOLTAIC SYSTEM OPTIMIZATION. Universidad Ciencia Y Tecnología, 24(100), 74-87. Retrieved from https://uctunexpo.autanabooks.com/index.php/uct/article/view/307

Abstract

The present investigation includes a review of optimization strategies of the photovoltaic systems oriented towards the axes of sustainable development, as well as technological concepts that include reconfiguration techniques of photovoltaic modules and monitoring of the maximum power point. Giving rise to the design of an alternative method of optimization, based on intelligent positioning of airships suspended at a certain height, in order to obtain a signal adapted to the conditions of maximum power and quality to neutralize the degradation of PV modules. This scheme proposes a minimal intervention of the installed systems concentrating the design to the heliostatic solar kites, studying the control and optimization model, in order to present less environmental impact and offer ecological solutions. Similarly, the bio-inspired training technology for solar tracking and waveguide concentration for the MPPT system is analyzed. The result is a strategy formulated with the description of the components, definition of the technology and support equations. The main contribution, a solution for re-addressing of solar radiation and extension of peak sun hours, on photovoltaic conversion surfaces, while proposing the protection of direct radiation on natural surfaces.

Keywords: Renewable Energies, Bio-Inspired Neural Training, Solar Heliostatic Comet, MPPT Optimization.

References

[1]C. Sandoval-Ruiz, “Modelo VHDL de Control Neuronal sobre tecnología FPGA orientado a Aplicaciones Sostenibles”. Ingeniare. Revista chilena de ingeniería, Vol. 27, No. 3, 2019, pp. 383-395. [En línea]. Disponible en: https://scielo.conicyt.cl/pdf/ingeniare/v27n3/0718-3305-ingeniare-27-03-00383.pdf. [Último acceso: 3 de noviembre de 2019]

[2]C. Sandoval-Ruiz, “Control de Micro-Redes de Energía Renovable a través de estructuras LFCS Reconfigurables en VHDL”. Ciencia y tecnología, Vol. 18, 2018, pp. 71-86. [En línea]. Disponible en: https://dspace.palermo.edu/ojs/index.php/cyt/article/view/847. [Last Access: November 3, 2019]

[3]C. Sandoval-Ruiz, “Códigos Reed Solomon para sistemas distribuidos de energías renovables y smart grids a través de dispositivos electrónicos inteligentes sobre tecnología FPGA”. Memoria Investigaciones en Ingeniería, Vol. 16, 2018, pp. 37-54. [Online]. Availble: http://revistas.um.edu.uy/index.php/ingenieria/article/view/296. [Last Access: November 3, 2019]

[4]C. Sandoval-Ruiz, “Plataforma de Investigación de Redes Eléctricas Reconfigurables de Energías Renovables aplicando Modelos LFSR”. Universidad, Ciencia y Tecnología, 23(95), 2019, pp. 103-115. http://uctunexpo.autanabooks.com/index.php/uct/article/view/253/409. 

[5]C.Sandoval-Ruiz, C. “Operador matemático LFC(n,k) en campos finitos basado en concatenación fractal para GF(2m) – Extendido”. Ciencia e Ingeniería, Vol. 41, No. 2, 2020, pp. 197-204. [Online]. Availble:
http://erevistas.saber.ula.ve/index.php/cienciaeingenieria/article/view/16055/21921927185. [Last Access: November 3, 2019]

[6]C. Sandoval-Ruiz, “Arreglos Fotovoltaicos Inteligentes con Modelo LFSR-Reconfigurable”. Ingeniería: Revista de la Universidad de Costa Rica, Vol. 30, No. 2, 2020, pp. 32-61. [Online]. Availble: https://revistas.ucr.ac.cr/index.php/ingenieria/article/view/39484. [Last Access: February 23, 2020]

[7]C. Sandoval-Ruiz, “Modelo LFSR de Optimización de Arreglos Solares Fotovoltaicos en Sistemas de ERNC Reconfigurable”. Revista Ingeniare. En revisión.

[8]C. Sandoval-Ruiz. “Arreglo Inteligente de Concentración Solar FV para MPPT usando Tecnología FPGA”. Revista Técnica Univerdidad del Zulia. En revisión.

[9]C. Sandoval-Ruíz, “Modelo Neuro-Adaptativo en VHDL, basado en circuitos NLFSR, para control de un Sistema Inteligente de Tecnología Sostenible”. Universidad, Ciencia Y Tecnología, Vol. 21, No. 85, 2017, pp. 140–149.

[10]C. Sandoval-Ruiz, “LFSR-Fractal ANN Model applied in R-IEDs for Smart Energy”. IEEE Latin America Transactions, VOL. Vol. 18, No. 4, 2020, pp. 677-686. [Online]. Availble: https://www.inaoep.mx/~IEEElat/index.php/transactions/article/view/1423/446. [Last Access: January 23, 2020]

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