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Volume 20
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10.3390/s20247242
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Article

Forecast Model Update Based on a Real-Time Data Processing Lambda Architecture for Estimating Partial Discharges in Hydrogenerator

by
Fabio Henrique Pereira
1,2,3,*,
Francisco Elânio Bezerra
2,
Diego Oliva
1,
Gilberto Francisco Martha de Souza
3,
Ivan Eduardo Chabu
3,
Josemir Coelho Santos
3,
Shigueru Nagao Junior
3 and
Silvio Ikuyo Nabeta
3
1
Informatics and Knowledge Management Graduate Program, Nove de Julho University—UNINOVE, São Paulo 01525-000, Brazil
2
Industrial Engineering Graduate Program, Nove de Julho University—UNINOVE, São Paulo 01525-000, Brazil
3
Polytechnic School, University of São Paulo—EPUSP, São Paulo 05508-010, Brazil
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(24), 7242; https://doi.org/10.3390/s20247242
Submission received: 23 November 2020 / Revised: 8 December 2020 / Accepted: 14 December 2020 / Published: 17 December 2020
(This article belongs to the Section Fault Diagnosis & Sensors)
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Abstract

The prediction of partial discharges in hydrogenerators depends on data collected by sensors and prediction models based on artificial intelligence. However, forecasting models are trained with a set of historical data that is not automatically updated due to the high cost to collect sensors’ data and insufficient real-time data analysis. This article proposes a method to update the forecasting model, aiming to improve its accuracy. The method is based on a distributed data platform with the lambda architecture, which combines real-time and batch processing techniques. The results show that the proposed system enables real-time updates to be made to the forecasting model, allowing partial discharge forecasts to be improved with each update with increasing accuracy.
Keywords: autoregressive forecasting model; lambda architecture; partial discharges; power hydrogenerators; real-time data processing autoregressive forecasting model; lambda architecture; partial discharges; power hydrogenerators; real-time data processing

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MDPI and ACS Style

Pereira, F.H.; Bezerra, F.E.; Oliva, D.; Souza, G.F.M.d.; Chabu, I.E.; Santos, J.C.; Junior, S.N.; Nabeta, S.I. Forecast Model Update Based on a Real-Time Data Processing Lambda Architecture for Estimating Partial Discharges in Hydrogenerator. Sensors 2020, 20, 7242. https://doi.org/10.3390/s20247242

AMA Style

Pereira FH, Bezerra FE, Oliva D, Souza GFMd, Chabu IE, Santos JC, Junior SN, Nabeta SI. Forecast Model Update Based on a Real-Time Data Processing Lambda Architecture for Estimating Partial Discharges in Hydrogenerator. Sensors. 2020; 20(24):7242. https://doi.org/10.3390/s20247242

Chicago/Turabian Style

Pereira, Fabio Henrique, Francisco Elânio Bezerra, Diego Oliva, Gilberto Francisco Martha de Souza, Ivan Eduardo Chabu, Josemir Coelho Santos, Shigueru Nagao Junior, and Silvio Ikuyo Nabeta. 2020. "Forecast Model Update Based on a Real-Time Data Processing Lambda Architecture for Estimating Partial Discharges in Hydrogenerator" Sensors 20, no. 24: 7242. https://doi.org/10.3390/s20247242

APA Style

Pereira, F. H., Bezerra, F. E., Oliva, D., Souza, G. F. M. d., Chabu, I. E., Santos, J. C., Junior, S. N., & Nabeta, S. I. (2020). Forecast Model Update Based on a Real-Time Data Processing Lambda Architecture for Estimating Partial Discharges in Hydrogenerator. Sensors, 20(24), 7242. https://doi.org/10.3390/s20247242

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