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Learning perceptions of Smart Grid class with laboratory for undergraduate students

  • Arturo Molina
  • Pedro Ponce
  • Germán Eduardo Baltazar ReyesEmail author
  • Luis Arturo Soriano
Original Paper
  • 5 Downloads

Abstract

Due to the modernization of the electrical grid and the commitments to it made by several governments and industries around the world, the work of engineers specialized in the electrical field is necessary more than ever. However, in recent years, the number of engineers working in this area has been decreasing, while almost half of their current population is prone to retirement. To solve this problem, universities began to modify their electrical engineering programs and courses, giving more focus to the implementation of Smart Grid technology. Although various approaches have been used in teaching methodologies to educate new engineers, it is also necessary to evaluate if the contents given in such classes are being properly taught. This paper proposes a new syllabus and new Smart Grid class, which is based on hand on experiments in a Smart Grid laboratory. This proposal promotes and trains undergraduate students in the use of the new technologies that are being deployed in the electrical industry nowadays, and it includes a discussion of the social, economic and environmental implications of the new ways to generate and distribute electrical power. To evaluate if the class methodology in our project was successfully implemented, a student perception survey was applied to analyze the way the undergraduate students perceived the Smart Grid class given to them. Additionally, signal detection theory and fuzzy logic type 1 and type 2 were used to compare their answers with the ones given by the professor as part of assessing the efficiency of the class syllabus and the teaching methodology for the purpose of improving their quality in future courses. The results obtained showed that the students acquired a synthesis of learning and analytical thinking to equip them with the competencies to solve the various challenges of electrical grid modernization. Additionally, the proposed new class methodology utilized innovative hands-on activities in laboratory practices that reinforced the learning of the most relevant theoretical concepts of the Smart Grid technology.

Keywords

Educational Innovation Fuzzy logic type 1 Fuzzy logic type 2 Perception Signal detection theory Smart Grid 

Notes

Acknowledgements

The authors would like to thank the support from Grant 266632, “Bi-national Laboratory on Smart Sustainable Energy Management and Technology Training,” from CONACYT; and to Tecnologico de Monterrey for the facilities given for the research and experimentation; and acknowledgement of the financial and technical support of Writing Lab, TecLabs and Tecnologico de Monterrey in the production of this work.

References

  1. 1.
    Tuballa, M.L., Abundo, M.L.: A review of the development of smart grid technologies. Renew. Sustain. Energy Rev. 59, 710–725 (2016)CrossRefGoogle Scholar
  2. 2.
    NIST Framework: Roadmap for smart grid interoperability standards, release 2.0 (2012). NIST Special Publication, 1108 (2016)Google Scholar
  3. 3.
    Kurstedt, P.: An American viewpoint on engineering education, pp. 23–28 (2001)Google Scholar
  4. 4.
    Bréchet, Y.J.M.: Interdisciplinary training of engineers—a challenge between superficiality and overspecialization, pp. 65–76 (2001)Google Scholar
  5. 5.
    Reuber, M., Klocke, F.: New demands on engineers—paths in education leading to professional qualifications, pp. 29–44 (2001)Google Scholar
  6. 6.
    Ponce, P., Polasko, K., Molina, A.: Technology transfer motivation analysis based on fuzzy type 2 signal detection theory. AI Soc. 31(2), 245–257 (2016)CrossRefGoogle Scholar
  7. 7.
    Shahidehpour M.: Smart grid education and workforce training center, pp. 1–3 (2011)Google Scholar
  8. 8.
    Anna University, Chennai. PS5091 SMART GRID (2018)Google Scholar
  9. 9.
    Austin Community College District: Syllabus—smart grid technology (2018)Google Scholar
  10. 10.
    Dr. A.P.J. Abdul Kalam Technical University. M. Tech. Electrical & Electronics Engineering 2016–2017 Syllabus.pdf (2018)Google Scholar
  11. 11.
    Gujarat Technological University: 2740703 Smart Grid Technology and Applications (2018)Google Scholar
  12. 12.
    KTH Royal Institute of Technology: KTH | EI2455 Smart Electrical Networks and Systems 7.5 credits (2018)Google Scholar
  13. 13.
    Lehigh University: CSE450/ECE450: Communications and Networking for Smart Grids (2018)Google Scholar
  14. 14.
    NM State University: CS 479/579: Introduction to Smart Grids (Fall 2016) (2018)Google Scholar
  15. 15.
    Rutgers School of Engineering: Smart Grid: Fundamental Elements of Design (2018)Google Scholar
  16. 16.
    Savitribai Phule Pune University: Syllabus for the B.E. electrical engineering (2018)Google Scholar
  17. 17.
    Slovak University of Technology in Bratislava: Course syllabus I-INSEMOB—Smart grid in e-mobility (FEEIT - WS 2016/2017) (2018)Google Scholar
  18. 18.
    Stanford Center for Professional Development: Course Syllabus—Smart Grid (XEIET137) (2018)Google Scholar
  19. 19.
    Tecnológico de Monterrey: TE3066 Redes eléctricas inteligentes (2018)Google Scholar
  20. 20.
    The University of Vermont: EE 217—Smart Grid (2018)Google Scholar
  21. 21.
    UC Berkeley Extension: Smart Grid Technology (2018)Google Scholar
  22. 22.
    University of California Riverside: Introduction to Smart Grid (2018)Google Scholar
  23. 23.
    University of Nevada, Las Vegas: ECG 743 SYLLABUS 2015 (2018)Google Scholar
  24. 24.
    University of Nicosia, Cyprus: OGEE-541 Smart Grid Management (2018)Google Scholar
  25. 25.
    University of Oulu: 488501s Smart grids 1: Integrating Renewable Energy Sources, 5 ECTS (2018)Google Scholar
  26. 26.
    University of Texas at Austin: Course EE 379 k/EE 394v Smart Grids (2018)Google Scholar
  27. 27.
    UNSW Australia: GSOE 9141 -Smart Grids and Distribution Networks (2018)Google Scholar
  28. 28.
    Kezunovic, M.: Teaching the smart grid fundamentals, using modeling, simulation, and hands-on laboratory experiments. In: Power and Energy Society General Meeting, 2010 IEEE, pp. 1–6. IEEE (2010)Google Scholar
  29. 29.
    Arthur James Swart: Student usage of a learning management system at an open distance learning institute: a case study in electrical engineering. Int. J. Electr. Eng. Educ. 52(2), 142–154 (2015)CrossRefGoogle Scholar
  30. 30.
    Tao, J., Han, H., Wen, X., Tang, J.: Entering the world of electrical engineering: a gateway course for first-year students at Wuhan University, China. Int. J. Electr. Eng. Educ. 54(2), 131–140 (2017)CrossRefGoogle Scholar
  31. 31.
    Bari, A., Jiang, J., Saad, W., Jaekel, A.: Challenges in the smart grid applications: an overview. Int. J. Distrib. Sens. Netw. 10(2), 1–11 (2014)CrossRefGoogle Scholar
  32. 32.
    Islam, M., Ruhul Amin, M.: Renewable-energy education for mechanical engineering undergraduate students. Int. J. Mech. Eng. Educ. 40(3), 207–219 (2012)CrossRefGoogle Scholar
  33. 33.
    Gordon, M., Shahidehpour, M.: A living laboratory [the business scene]. IEEE Power Energy Mag. 9(1), 18–28 (2011)CrossRefGoogle Scholar
  34. 34.
    Deese, A.S.: Development of smart electric power system (SEPS) laboratory for advanced research and undergraduate education. IEEE Trans. Power Syst. 30(3), 1279–1287 (2015)CrossRefGoogle Scholar
  35. 35.
    Srinivasan, D.: Teaching sustainable energy course through real world case studies, projects and simulations. In: 2016 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), pp. 436–440. IEEE (2016)Google Scholar
  36. 36.
    Strasser, T., Stifter, M., Andren, F., Palensky, P.: Co-simulation training platform for smart grids. IEEE Trans. Power Syst. 29(4), 1989–1997 (2014)CrossRefGoogle Scholar
  37. 37.
    Celeita, D., Hernandez, M., Ramos, G., Penafiel, N., Rangel, M., Bernal, J.D.: Implementation of an educational real-time platform for relaying automation on smart grids. Electr. Power Syst. Res. 130, 156–166 (2016)CrossRefGoogle Scholar
  38. 38.
    Maseda, F.J., Martija, I., Martija, I.: D.C. motor-generator training tool with solar recharge and braking energy recovery. Int. J. Electr. Eng. Educ. 50(1), 80–95 (2013)CrossRefGoogle Scholar
  39. 39.
    Wiezorek, C., Parisio, A., Kyntäjä, T., Elo, J., Gronau, M., Johannson, K.H., Strunz, K.: Multi-location virtual smart grid laboratory with testbed for analysis of secure communication and remote co-simulation: concept and application to integration of Berlin, Stockholm, Helsinki. IET Gener. Transm. Distrib. 15, 3134–3143 (2017)CrossRefGoogle Scholar
  40. 40.
    McNaught, C., Lam, P., Cheng, K.F., Kennedy, D.M., Mohan, J.B.: Challenges in employing complex e-learning strategies in campus-based universities. Int. J. Technol. Enhanc. Learn. 1(4), 266–285 (2009)CrossRefGoogle Scholar
  41. 41.
    Faza, A., Batarseh, M., Abu-Elhaija, W.: Upgrading power and energy engineering curricula in Jordanian universities: a case study at PSUT. Int. J. Electr. Eng. Educ. 54(1), 57–81 (2017)CrossRefGoogle Scholar
  42. 42.
    Wollenberg, B., Mohan, N.: The importance of modern teaching labs. IEEE Power Energy Mag. 8(4), 44–52 (2010)CrossRefGoogle Scholar
  43. 43.
    Smart Grid Mandate: Standardization mandate to European standardisation organisations (ESOS) to support European smart grid deployment. Technical report, European Commission (2011)Google Scholar
  44. 44.
    Tecnologico de Monterrey: Tec21 educational model (2018)Google Scholar
  45. 45.
    Wayne State University: Power and Energy Engineering—Electrical and Computer Engineering. Wayne State University (2018)Google Scholar
  46. 46.
    University of North Dakota: Electrical Engineering (2018)Google Scholar
  47. 47.
    Borlase, S.: Smart Grids: Infrastructure, Technology, and Solutions. CRC Press, Boca Raton (2016)Google Scholar
  48. 48.
    De Lorenzo Group: De Lorenzo Group (2018)Google Scholar
  49. 49.
    Kember, D., Wong, A.: Implications for evaluation from a study of students’ perceptions of good and poor teaching. High. Educ. 40(1), 69–97 (2000)CrossRefGoogle Scholar
  50. 50.
    McKenna, A.F., Hynes, M.M., Johnson, A.M., Carberry, A.R.: The use of engineering design scenarios to assess student knowledge of global, societal, economic, and environmental contexts. Eur. J. Eng. Educ. 41(4), 411–425 (2016)CrossRefGoogle Scholar
  51. 51.
    Turner, M.J.: Design and development of a smart grid laboratory for an energy and power engineering technology program. Int. J. Electr. Eng. Educ. 54(4), 299–318 (2017)CrossRefGoogle Scholar
  52. 52.
    Abdi, H.: Signal detection theory (SDT). In: Encyclopedia of Measurement and Statistics, pp. 886–889 (2007)Google Scholar
  53. 53.
    Pashler, H.: Stevens’ Handbook of Experimental Psychology, vol. 4. Wiley, New York (2002)CrossRefGoogle Scholar
  54. 54.
    Parasuraman, R., Masalonis, A.J., Hancock, P.A.: Fuzzy signal detection theory: Basic postulates and formulas for analyzing human and machine performance. Hum. Factors 42(4), 636–659 (2000)CrossRefGoogle Scholar
  55. 55.
    Cruz, P.P.: Inteligencia artificial con aplicaciones a la ingeniería. Alfaomega (2011)Google Scholar
  56. 56.
    Lotfi Asker Zadeh: The concept of a linguistic variable and its application to approximate reasoning—I. Inf. Sci. 8(3), 199–249 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  57. 57.
    Mendel, J.M., John, R.I., Liu, F.: Interval type-2 fuzzy logic systems made simple. IEEE Trans. Fuzzy Syst. 14(6), 808–821 (2006)CrossRefGoogle Scholar
  58. 58.
    Karnik, N.N., Mendel, J.M.: Centroid of a type-2 fuzzy set. Inf. Sci. 132(1–4), 195–220 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  59. 59.
    Ponce-Cruz, P., Molina, A., MacCleery, B.: Fuzzy Logic Type 1 and Type 2 Based on LabVIEWTM FPGA. Springer, New York (2016)CrossRefGoogle Scholar
  60. 60.
    Wu, D., Mendel, J.M.: Enhanced Karnik–Mendel algorithms. IEEE Trans. Fuzzy Syst. 17(4), 923–934 (2009)CrossRefGoogle Scholar
  61. 61.
    Lattuca, L., Terenzini, P.: Survey of under-graduate engineering students. Engineer of 2020 survey (2014)Google Scholar

Copyright information

© Springer-Verlag France SAS, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Engineering and SciencesTecnologico de MonterreyMexico CityMexico

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