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

作     者：Pereira, Fabio Henrique Bezerra, Francisco Elanio Oliva, Diego Martha de Souza, Gilberto Francisco Chabu, Ivan Eduardo Santos, Josemir Coelho Nagao Junior, Shigueru Nabeta, Silvio Ikuyo 

作者机构：Nove de Julho Univ UNINOVE Informat & Knowledge Management Grad Program BR-01525000 Sao Paulo Brazil Nove de Julho Univ UNINOVE Ind Engn Grad Program BR-01525000 Sao Paulo Brazil Univ Sao Paulo EPUSP Polytech Sch BR-05508010 Sao Paulo Brazil 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2020年第20卷第24期

页      面：7242页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：National Council for Scientific and Technological Development (CNPq) - hydroelectric plant INVESTCO-UHE Lajeado through the Brazilian Electricity Regulatory Agency (ANEEL) RD program 

主　　题：autoregressive forecasting model lambda architecture partial discharges power hydrogenerators real-time data processing 

摘      要：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.