A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature Prediction

A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature Prediction

Transformers are essential for voltage regulation and power distribution in electrical systems, and monitoring their top-oil temperature is crucial for detecting potential faults. High oil temperatures are directly linked to insulation degradation, a primary cause of transformer failures. Therefore,...

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Bibliographic Details
Main Authors: Xuemin Huang, Xiaoliang Zhuang, Fangyuan Tian, Zheng Niu, Yujie Chen, Qian Zhou, Chao Yuan
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Energies
Subjects:
transformer top-oil temperature
hybrid time series forecasting model
ARIMA-LSTM-XGBoost
prediction accuracy
fault detection
Online Access:https://www.mdpi.com/1996-1073/18/6/1432
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