Automatization of Solar Concentrator Manufacture and Assembly

  • Tetyana Baydyk
  • Ernst Kussul
  • Donald C. Wunsch II
Part of the Computational Intelligence Methods and Applications book series (CIMA)


This section discusses an automatic adjustment system for the height of the support elements, arranged in a specific structure to achieve the approximation of a parabolic surface with the triangular mirrors of a solar concentrator. Wood’s 1982 patent [1] describes a triangular, flat-mirror parabolic concentrator that uses screws to adjust the heights of the vertices of each triangular mirror, and each screw can move six vertices of the neighboring triangles. The height is adjusted to direct the reflected solar beam from the mirrors to a focal point. This process is very complicated because the movement of one screw simultaneously affects six neighboring mirrors. Additionally, focusing all of the mirrors is necessary to solve many linear equations explicitly, or to use iterative approaches. It has been demonstrated in earlier chapters that one method for reducing the costs is to use flat-facet low-cost mirrors to approximate the parabolic dish. Increasing the number of flat-facet mirrors improves the approximation and efficiency, but also increases the cost of manufacturing. More mirrors also require more time to individually place all mirrors. Thus, automation is a logical method for reducing the cost. Computer vision plays an important role in automation because it enables the detection of pieces, their positioning at the support frame, and quality control. The performance of this task can be improved by combining computer vision with artificial neural networks.


  1. 1.
    Wood, D.: Support Structure for a Large Dimension Parabolic Reflector and Large Dimension Parabolic Reflector. EP 002288 Al, 21.12.1982 (24.07.1979) (1982)Google Scholar
  2. 2.
    Estufa solar para poblaciones urbanas, del Departamento de Ingeniería Eléctrica del Centro de Investigación y Estudios Avanzados (CINVESTAV) México. Accessed 10 May 2016
  3. 3.
    Kussul, E., et al.: Method and device for mirrors position adjustment of a solar concentrator, notice of allowance, 02.03.2010 (Mexico), 02.03.2011 (USA). USA Patent N US 8,631,995 B2, 21 Jan 2014 (2014)Google Scholar
  4. 4.
    The First Decade: 2004–2014, 10 Years of Renewable Energy Progress, Renewable Energy Policy Network for the 21st Century. Accessed 02 Oct 2016
  5. 5.
    Technology Roadmap Solar Thermal Electricity 2014. International Energy Agency. Accessed 02 Oct 2016
  6. 6.
    Lovegrove, K., Burgess, G., Pye, J.: A new 500m2 paraboidal dish solar concentrator. Sol. Energy. 85, 620–626 (2011)CrossRefGoogle Scholar
  7. 7.
    The Year of Concentrating Solar Power. U.S. Department of Energy. (2014)
  8. 8.
    Escobedo-Alatorre, J., Tecpoyotl-Torres, M., Martınez, O.G., Vera-Dimas, J.G., Campos-Alvarez, J., Torres-Cisneros, M., Sanchez-Mondragon, A.: Prototype of plannar autonomous solar concentrator. In: Proceedings of 3rd Conference of University of Guanajuato, IEEE students chapter, Salamanca, Gto, November 2009, pp. 33–36Google Scholar
  9. 9.
    Vivar, M., Daniel, J., Skryabin, I.L., Everett, V.A., Blakers, A.W., Suganthi, L., Iniyan, S.: A hybrid solar linear concentrator prototype in India. In: Photovoltaic Specialist Conference (PVSC), 35th IEEE, June 2010, pp. 3092–3097Google Scholar
  10. 10.
    Li, L., Dubowsky, S.: A new design approach for solar concentrating parabolic dish based on optimized flexible petals. Mech. Mach. Theory. 46, 1536–1548 (2011)CrossRefGoogle Scholar
  11. 11.
    Franco, J., Saravia, L., Javi, V., Caso, R., Fernandez, C.: Pasteurization of goat milk using a low cost solar concentrator. Sol. Energy. 82, 1088–1094 (2008)CrossRefGoogle Scholar
  12. 12.
    Khuchua, N., Melkadze, R., Moseshvili, A.: New-type solar concentrator concept – Approach to reduced-cost CPV module technology. In: 42nd IEEE Photovoltaic Specialist Conference (PVSC) (2015)Google Scholar
  13. 13.
    Kussul, E., Baydyk, T., Saniger Blesa, J.M., Bruce Davidson, N.C., Lara Rosano, F.J., Rodriguez Mendoza, J.L.: Dispositivo de soporte para concentrador solar con espejos planos. Spanish Patent ES2 525 276, 25 Sept 2015Google Scholar
  14. 14.
    Kussul, E., Baydyk, T., Saniger Blesa, J.M., Bruce Davidson, N.C., Lara Rosano, F.J., Rodriguez Mendoza, J.L.: Dispositivo de soporte para concentrador solar con espejos planos. Mexican Patent 334 742, 09 Oct 2015Google Scholar
  15. 15.
    Kussul, E., Baydyk, T., Lara Rosano, F.J., Saniger Blesa, J.M., Bruce, N.: Concentrador solar. Mexican Patent 309 274, 26 Apr 2013Google Scholar
  16. 16.
    Cunha, J., Ferreira, R., Lau, N.: Computer vision and robotic manipulation for automated feeding of Cork Drillers. Mater. Des. 82, 290–296 (2015)CrossRefGoogle Scholar
  17. 17.
    Wei, X., Jia, K., Lan, J., Li, Y., Zeng, Y., Wang, C.: Automatic method of fruit object extraction under complex agricultural background for vision system of fruit picking robot. Optik. 125, 5684–5689 (2014)CrossRefGoogle Scholar
  18. 18.
    Tanigaki, K., Fujiura, T., Akase, A., Imagawa, J.: Cherry harvesting robot. Comput. Electron. Agric. 63, 65–72 (2008)CrossRefGoogle Scholar
  19. 19.
    De Oliveira, E.M., Leme, D.S., Barbosa, B.H.G., Rodarte, M.P., Pereira, R.G.F.A.: A computer vision system for coffe beans classification based on computational intelligence techniques. J. Food Eng. 171, 22–27 (2016)CrossRefGoogle Scholar
  20. 20.
    Shafiee, S., Minaei, S., Moghaddam-Charkari, N., Barzegar, M.: Honey characterization using computer vision system and artificial neural networks. Food Chem. 159, 143–150 (2014)CrossRefGoogle Scholar
  21. 21.
    Kussul, E., Baidyk, T., Wunsch, D.: Neural Networks and Micro Mechanics, pp. 210. Springer, ISBN 978-3-642-02534-1 (2010)Google Scholar
  22. 22.
    Baidyk, T., Kussul, E.: Redes neuronales, visión computacional y micromecánica, pp. 158. Editoriales ITACA-UNAM (2009)Google Scholar
  23. 23.
    Baidyk, T., et al.: Texture recognition with random subspace neural classifier. WSEAS Trans. Circuits Syst. 4(4), 319–325 (2005)MathSciNetGoogle Scholar
  24. 24.
    Makeyev, O., et al.: Limited receptive area neural classifier for texture recognition of mechanically treated metal surfaces. Neurocomputing. 71(7–9), 1413–1421 (2008)CrossRefGoogle Scholar
  25. 25.
    Baydyk, T., Kussul, E., Robles Roldan, M.A.: Automation of Manufacturing and Assembly of Low-Cost Solar Concentrators, ICCE 2015, Ottawa, Canada, 14–16 September 2015, pp. 28–35Google Scholar
  26. 26.
    Robles Roldan, M.A., Baydyk, T., Kussul, E.: Desarrollo de Software para Reconocimiento de Imágenes Basado en Redes Neuronales, 4to Congreso Internacional de Investigación e Innovación en Ingeniería de Software 2016, CONISOFT 2016, Puebla, México, 27 al 29 de abril 2016, pp. 119–125Google Scholar
  27. 27.
    Kussul, E., Makeyev, O., Baidyk, T., et al.: The problem of automation of solar concentrator assembly and adjustment. Int. J. Adv. Robot. Syst. 8(4), 150–157 (2011)CrossRefGoogle Scholar
  28. 28.
    Baydyk, T., Kussul, E.: Small Flat Facet Solar Concentrators, 3rd International Conference & Exhibition on Clean Energy, ICCE 2014, Quebec City, Canada, 20–22 October, pp. 112–120 (2014)Google Scholar
  29. 29.
    Johnston, G.: Focal region measurements of the 20 m2 tiled dish at dic Australian National University. Sol. Energy. 63(2), 117–124 (1998)CrossRefGoogle Scholar
  30. 30.
    Celik, H.I., et al.: Development of a machine vision system: real-time fabric detection and classification with neural networks. J. Text. Inst. 105(6), 575–585 (2014)CrossRefGoogle Scholar
  31. 31.
    Ming Tsai, D.: Boundary-based corner detection using neural networks. Pattern Recogn. 30(1), 85–97 (1997)CrossRefGoogle Scholar
  32. 32.
    Subri, S.H., et al.: Neural network corner detection of vertex chain code. AIML J. 6(1), 37–43 (2006)Google Scholar
  33. 33.
    Meftah, B., et al.: Segmentation and edge detection based on spiking neural network model. Neural. Process. Lett. 32(2), 131–146 (2010)MathSciNetCrossRefGoogle Scholar
  34. 34.
    El-Sayed, M.A., et al.: Automated edge detection using convolutional neural network. Int. J. Adv. Comput. Sci. Appl. 4(10), 11–17 (2013)Google Scholar
  35. 35.
    Fukushima, K.: Neocognitron: a self-organizing neural for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36, 193–202 (1980)CrossRefGoogle Scholar
  36. 36.
    Baidyk, T., et al.: Flat image recognition in the process of microdevice assembly. Pattern Recogn. Lett. 25(1), 107–118 (2004)CrossRefGoogle Scholar
  37. 37.
    Toledo-Ramirez, G.K., Kussul, E., Baidyk, T.: Work piece recognition based on the permutation neural classifier technique. Mach. Vis. Appl. 22(3), 495–504 (2011). CrossRefGoogle Scholar
  38. 38.
    Kussul, E., et al.: Development of micromachine tool prototypes for microfactories. J. Micromech. Microeng. 12, 795–813 (2002)CrossRefGoogle Scholar
  39. 39.
    Jain, R., et al.: Machine Vision. McGraw Hill, New York (1995)Google Scholar
  40. 40.
    Faugeras, O.: Three-Dimensional Computer Vision. MIT Press, Cambridge, MA (1993)Google Scholar
  41. 41.
    Ehrenmann, M., et al.: A Comparison of Four Fast Vision Based Object Recognition Methods for Programming by Demonstration Applications, Proceedings of the IEEE International Conference on Robotics & Automation, San Francisco, CA, USA, pp. 1862–1867 (2000)Google Scholar
  42. 42.
    Baydyk, T., Kussul, E., Robles Roldan, M.A.: New Approach to Design of Flat Facet Solar Concentrators, ICCE 2016, Montreal, Canada, 22–24 August 2016, pp. 1–7Google Scholar
  43. 43.
    White Cliffs Solar Power Station. Accessed 10 May 2016

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tetyana Baydyk
    • 1
  • Ernst Kussul
    • 1
  • Donald C. Wunsch II
    • 2
  1. 1.Instituto de Ciencias Aplicadas y Tecnología (ICAT)Universidad Nacional Autónoma de México (UNAM)Mexico CityMexico
  2. 2.Dept. of Electrical and Computer EngineeringMissouri University of Science and TechnologyRollaUSA

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