Transmisión y Reconstrucción del Perfil Eléctrico de Carga Usando Sensado Compresivo Transmisión sobre un canal inalámbrico real usando SDRs

O. S. Penaherrera-Pulla, J. L. Delgado-Tello, Juan Paul Inga-Ortega, C. Gomez-Santamaria

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

Resumen

The information generated from Smart Meters in advanced measurement infrastructure (AMI) in a Smart Grid, needs real-time data acquisition and processing, this could generate high redundancy of data in an uplink in the communication network compared to the number of smart meters in a city. In this sense, the application of Compressive Sensing (CS) can improve the efficiency in the transfer of data and reduce a possible overload in the system. However, it is necessary to establish the minimum conditions required for the use of CS for data related to the Electrical Energy Consumption Profile. Thus, this article presents the analysis of the use of CS as a source compression method to reduce the data to be transmitted on a network. So, in order to evaluate the quality of the load profile reconstruction using CS despite the transformations that the data undergoes in a real wireless communication system, this technique has been implemented on a Software Defined Radio System (SDR). For this, the measurement matrix implemented with Gauss and Bernoulli distributions is analyzed. The algorithms for the reconstruction are: OMP (Paired orthogonal search), BP (Base search) and GPSR (Gradient Projection Sparse Recovery) in different compression ratios (10 - 80%). It also shows the comparison of errors generated in the uses for the representation of signals in the Gaussian campaign, the DCT transform, the Wavelet Meyer transform and Wavelet Biorthogonal 3.9.

Título traducido de la contribuciónTransmission and Reconstruction of the electrical load profile using compressive sensing on a real wireless channel with SDR: Transmission over a real wireless channel using SDRs
Idioma originalEspañol
Título de la publicación alojadaProceedings of CISTI 2020 - 15th Iberian Conference on Information Systems and Technologies
EditoresAlvaro Rocha, Bernabe Escobar Perez, Francisco Garcia Penalvo, Maria del Mar Miras, Ramiro Goncalves
EditorialIEEE Computer Society
ISBN (versión digital)9789895465903
DOI
EstadoPublicada - jun 2020
Evento15th Iberian Conference on Information Systems and Technologies, CISTI 2020 - Seville, Espana
Duración: 24 jun 202027 jun 2020

Serie de la publicación

NombreIberian Conference on Information Systems and Technologies, CISTI
Volumen2020-June
ISSN (versión impresa)2166-0727
ISSN (versión digital)2166-0735

Conferencia

Conferencia15th Iberian Conference on Information Systems and Technologies, CISTI 2020
País/TerritorioEspana
CiudadSeville
Período24/06/2027/06/20

Nota bibliográfica

Publisher Copyright:
© 2020 AISTI.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Palabras clave

  • AMI
  • BP
  • Compressive Sensing
  • Electrical Consumption Profile
  • GPSR
  • OMP
  • SDR
  • Smart Grids

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