Informatics and Knowledge Management Graduate Program, Nove de Julho University-UNINOVE, São Paulo 01525-000, Brazil.
Industrial Engineering Graduate Program, Nove de Julho University-UNINOVE, São Paulo 01525-000, Brazil.
Sensors (Basel). 2020 Dec 17;20(24):7242. doi: 10.3390/s20247242.
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.
水轮发电机局部放电的预测取决于通过传感器收集的数据和基于人工智能的预测模型。然而，由于收集传感器数据的成本高，以及实时数据分析不足，预测模型是用一组历史数据进行训练的，无法自动更新。本文提出了一种更新预测模型的方法，旨在提高其准确性。该方法基于具有 lambda 架构的分布式数据平台，结合了实时和批处理技术。结果表明，所提出的系统能够实时更新预测模型，通过每次更新，随着精度的提高，局部放电预测得以改善。
