Revisión de aplicaciones de técnicas de monitoreo no intrusivo de cargas en redes eléctricas inteligentes

  1. Donato, Patricio G. 1
  2. Hernández, Álvaro 2
  3. Funes, Marcos 1
  4. Carugati, Ignacio 1
  5. Nieto, Ruben 3
  6. Ureña, Jesús 2
  1. 1 Instituto de Investigaciones Científicas y Tecnológicas en Electrónica (ICYTE)
  2. 2 Departamento de Electrónica, Universidad de Alcalá
  3. 3 Universidad Rey Juan Carlos
    info

    Universidad Rey Juan Carlos

    Madrid, España

    ROR https://ror.org/01v5cv687

Show affiliations +
Journal:
Ciencia y tecnología

ISSN: 1850-0870,  2344-9217

Year of publication: 2022

Issue: 22

Type: Article

DOI: 10.18682/CYT.VI22.5375 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: Ciencia y tecnología

Abstract

The Smart Grids concept is transforming the relationship of customers with the electricity in different ways. This paper provides a general overview of some potential applications to be developed under this conceptual framework, which have as a common denominator the use of non-intrusive load monitoring techniques. These techniques make it possible to disaggregate consumption based on specific measurements at certain locations in the electricity grid, without implement measurement points in each device to be monitored. Some of these new functionalities are particularly relevant for electricity grids in developing countries, which present complex challenges and need modernisation, while others are motivated by specific demands in developed countries. In all cases, the use of non-intrusive load monitoring techniques opens up new fields of applied research and technological development ranging from power grids to social issues.

Bibliographic References

  • Alcalá, J.M.; Ureña, J.; Hernández, A. y Gualda, D. (2017). Assessing Human Activity in Elderly People Using Non-Intrusive Load Monitoring. Sensors, (17), 351
  • Alcalá Orzáez, J.M. (2017). Non-Intrusive Load Monitoring techniques for Activity of Daily Living recognition, PhD Thesis, Universidad de Alcalá, España
  • Belley, C., Gaboury, S., Bouchard, B. y Bouzouane, A. (2014). An efficient and inexpensive method for activity recognition within a smart home based on load signatures of appliances. Pervasive and Mobile Computing, no. 12, pp. 58-78
  • Bollen, M.H.J. y Gu, I.Y.H. (2006). Signal processing of power quality disturbances. Wiley-IEEE Press, New Jersey
  • Brinkel, N.; Gerritsma, M..; AlSkaif, T.; Lampropoulos, I.; van Voorden, A. M.; Fidder, H. y van Sark, W. (2020). Impact of rapid PV fluctuations on power quality in the low-voltage grid and mitigation strategies using electric vehicles. Int. J. Electrical Power & Energy Systems, vol 118
  • Chang, H.-H. y Linh, N.V. (2017). Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems. Energies 10, 611
  • Donato, P.G., Carugati, I., Strack, J.L., Orallo, C.O. y Funes, M.A. (2021). Detección y clasificación automática de eventos de tensión para el diagnóstico de fallas en redes de distribución. XIX Reunión de Trabajo en Procesamiento de la Información y Control (XIX RPIC 2021). San Juan, Argentina
  • Fiaschetti, L., Risso, M. y Boroni, G. (2019). Fault location index for distribution power system restoration using voltage sags. Int. Trans. Electrical Energy Systems
  • Fortin-Simard, D., Bilodeau, J., Gaboury, S., Bouchard, B. y Bouzouane A. (2014). Human activity recognition in smart homes: Combining passive RFID and load signatures of electrical devices. 2014 IEEE Sym. on Intelligent Agents, 22-29
  • Hart, G.W., Kern Jr., E. y Schweppe, F.C. (1989). Non-intrusive appliance monitor apparatus. U.S.A.
  • Hart, G.W. (1992) Nonintrusive appliance load monitoring. Proc. of the IEEE, vol. 80, (12), 1870–1891
  • He, D.; Lin, W.; Liu, N.; Harley, R. y Habetler, T. (2013). Incorporating Non-Intrusive Load Monitoring Into Building Level Demand Response. IEEE Trans. Smart Grids, (4), 1870-1877
  • Jimenez, V.A., Will, A. y Rodriguez, S. (2020). Phase identification and substation detection using data analysis on limited electricity consumption measurements. Electric Power Systems Research, vol. 187
  • Le, D.P., Bui, D.M., Ngo, C.C. y Le, A.M. (2018). FLISR Approach for Smart Distribution Networks Using E-Terra Software—A Case Study. Energies, 11, 3333
  • Loyarte, A., Sangoi, E., Clementi, L. y Vega, J. (2017). Optimal distribution of battery banks in microgrids with high photovoltaic penetration. XVII Work. Information Processing and Control (RPIC), Mar del Plata
  • Nalmpantis, C. y Vrakas, D. (2019). Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation. Artificial Intelligence Review, 52(1), 217-243
  • Patrono, L., Rametta, P. y Meis, J. (2018). Unobtrusive Detection of Home Appliance's Usage for Elderly Monitoring. 3rd Int. Conf. Smart and Sustainable Technologies
  • Pau, G.; Collotta, M.; Ruano, A. y Qin, J. (2017). Smart Home Energy Management. Energies, (10), 382
  • Spalding, R., Rosa, L., Almeida, C., Morais, R. y Gouvea, M. (2016). Fault Location, Isolation and service restoration (FLISR) functionalities tests in a Smart Grids laboratory for evaluation of the quality of service. 17th Int. Conf. Harmonics and Quality of Power (ICHQP), Belo Horizonte, 879-884
  • Xu, M., Li, R. y Li, F. (2016). Phase Identification With Incomplete Data. IEEE Trans. Smart Grids, 9(4), 2777-2785
Data source: Dialnet