Event-driven data acquisition for electricity metering : a tutorial
de Castro Tomé, Mauricio; Gutierrez-Rojas, Daniel; Nardelli, Pedro H. J.; Kalalas, Charalampos; da Silva, Luiz Carlos Pereira; Pouttu, Ari (2022-01-27)
M. d. Castro Tomé, D. Gutierrez-Rojas, P. H. J. Nardelli, C. Kalalas, L. C. P. d. Silva and A. Pouttu, "Event-Driven Data Acquisition for Electricity Metering: A Tutorial," in IEEE Sensors Journal, vol. 22, no. 6, pp. 5495-5503, 15 March15, 2022, doi: 10.1109/JSEN.2022.3147016
© The Author(s) 2022. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe2022051034122
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Abstract
This paper provides a tutorial on the most recent advances of event-driven metering (EDM) while indicating potential extensions to improve its performance. We have revisited the effects on signal reconstruction of (i) a fine-tuned procedure for defining power variation events, (ii) consecutive-measurements filtering that refers to the same event, (iii) spike filtering, and (iv) timeout parameter. We have illustrated via extensive numerical results that EDM can provide high-fidelity signal reconstruction while decreasing the overall number of acquired measurements to be transmitted. Its main advantage is to only store samples that are informative based on predetermined events, avoiding redundancy and decreasing the traffic offered to the underlying communication network. This tutorial highlights the key advantages of EDM and points out promising research directions.
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