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An embedded system approach for energy monitoring and analysis in industrial processes

Author

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  • Back, Jaime André
  • Tedesco, Leonel Pablo
  • Molz, Rolf Fredi
  • Nara, Elpidio Oscar Benitez

Abstract

This paper presents the development of a low cost embedded system targeted to energy management in industrial environments. The main objective of this study is a methodology for monitoring, acquisition and analysis of electrical parameters using digital signal processing (DSP) techniques. For this task, a system (hardware and software) responsible for the acquisition records the input signals using low cost technologies, such as microcontrollers, DSP platforms and embedded system like ARM System-on-Chip (SoC) Architecture. Finally, for data analysis, two applications were created: one using the Matlab® software, and another derived from the first, to run on the embedded device. Both applications used Discrete Wavelet Transform as primary technique of disturbances detection. As a result, it is shown a complete system for monitoring and analysis of electrical parameters, serving as a tool to aid the energy management in industrial processes.

Suggested Citation

  • Back, Jaime André & Tedesco, Leonel Pablo & Molz, Rolf Fredi & Nara, Elpidio Oscar Benitez, 2016. "An embedded system approach for energy monitoring and analysis in industrial processes," Energy, Elsevier, vol. 115(P1), pages 811-819.
    • Handle: RePEc:eee:energy:v:115:y:2016:i:p1:p:811-819
    DOI: 10.1016/j.energy.2016.09.045
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    References listed on IDEAS

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    Cited by:

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    2. Tehrani, Z. & Thomas, D.J. & Korochkina, T. & Phillips, C.O. & Lupo, D. & Lehtimäki, S. & O'Mahony, J. & Gethin, D.T., 2017. "Large-area printed supercapacitor technology for low-cost domestic green energy storage," Energy, Elsevier, vol. 118(C), pages 1313-1321.
    3. Schwambach, Gislene Cássia S. & López, Óscar Hernández & Sott, Michele Kremer & Carvalho Tedesco, Leonel Pablo & Molz, Rolf Fredi, 2022. "Acceptance and perception of wearable technologies: A survey on Brazilian and European companies," Technology in Society, Elsevier, vol. 68(C).
    4. BahooToroody, Ahmad & De Carlo, Filippo & Paltrinieri, Nicola & Tucci, Mario & Van Gelder, P.H.A.J.M., 2020. "Bayesian regression based condition monitoring approach for effective reliability prediction of random processes in autonomous energy supply operation," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    5. Francisco G. Montoya & Raul Baños & Alfredo Alcayde & Maria G. Montoya & Francisco Manzano-Agugliaro, 2018. "Power Quality: Scientific Collaboration Networks and Research Trends," Energies, MDPI, vol. 11(8), pages 1-16, August.

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