电力工程技术2026，Vol.45Issue(3)：37-45,56,10.DOI:10.12158/j.2096-3203.2026.03.005
基于Temporal Fusion Transformer模型的变压器油中溶解气体预测方法
Prediction method for dissolved gas in transformer oil based on the Temporal Fusion Transformer model
摘要
Abstract
Dissolved gas analysis in transformer oil is regarded as an important indicator for evaluating the operational status of transformers.Accurate prediction of trends in dissolved gases in oil is beneficial for preventing power transformer failures.A Temporal Fusion Transformer(TFT)model,optimized via Optuna hyperparameter tuning,is proposed to address the technical challenge of low prediction efficiency inherent in traditional models that rely on a single variable.Static variables including transformer group,winding phase,and gas type are introduced into the model,and an interpretable multi-head attention mechanism is integrated as well.Synchronous prediction of all dissolved gases in the oil of multiple transformers is thereby achieved,improving the early warning efficiency of substation operation and maintenance systems.An average relative error of only 0.306%is achieved by the proposed model,representing a 66.7%reduction relative to the Transformer baseline model.Higher predictive accuracy is also demonstrated in both short-term and long-term forecasting.In addition,the model's training time is only one quarter that of the Transformer baseline model.This efficiency aligns with the current trend toward simultaneous prediction across multiple device groups in intelligent early-warning platforms.Strong correlations between hydrogen and methane and between carbon dioxide and methane are indicated by the model's multi-head attention mechanism.These correlations are consistent with the gas generation patterns of oil-paper insulation degradation,further demonstrating the model's good interpretability and providing technical support for synchronous prediction in multiple device groups.关键词
电力变压器/油中溶解气体/同步预测/Temporal Fusion Transformer(TFT)模型/时间序列/注意力机制Key words
electric power transformer/dissolved gas in transformer oil/synchronous prediction/Temporal Fusion Transformer(TFT)model/time series/attention mechanism分类
信息技术与安全科学引用本文复制引用
周延豪,范路,任海龙,赵谡,王亚林,尹毅..基于Temporal Fusion Transformer模型的变压器油中溶解气体预测方法[J].电力工程技术,2026,45(3):37-45,56,10.基金项目
国家自然科学基金资助项目(52407024) （52407024）
