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

Autor:	Fabio Henrique Pereira, Francisco Elânio Bezerra, Diego Oliva, Gilberto Francisco Martha de Souza, Ivan Eduardo Chabu, Josemir Coelho Santos, Shigueru Nagao Junior, Silvio Ikuyo Nabeta
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	autoregressive forecasting model lambda architecture partial discharges power hydrogenerators real-time data processing Chemical technology TP1-1185
Zdroj:	Sensors, Vol 20, Iss 24, p 7242 (2020)
Druh dokumentu:	article
ISSN:	1424-8220
DOI:	10.3390/s20247242
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/f763596646204fa1b04addd58c3076e0 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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