基于注意力—门控循环—时间卷积网络的变压器油温预测

张京娥; 程中杰; 刘宝

doi:10.13296/j.1001-1609.hva.2026.08.008

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基于注意力—门控循环—时间卷积网络的变压器油温预测

研究与分析 | 更新时间：2026-08-12

- 基于注意力—门控循环—时间卷积网络的变压器油温预测
- Transformer Oil Temperature Prediction Based on Attention-gated Recurrent-temporal Covolutional Network
- 高压电器 2026年62卷第8期页码：58-66
- 作者机构：
  
  甘肃钢铁职业技术学院信息工程系，甘肃嘉峪关 735100
  西安科技大学电气与控制工程学院，西安 710054
- 作者简介：
  
  张京娥（1981—），女，硕士，副教授，研究方向为电气自动化（Email：13830791196@163. com）。
  刘宝（1983—），男，博士，副教授，研究方向为目标跟踪、信息融合、图像处理（通信作者）（E-mail：xiaobei0077@163. com）。
- 基金信息：
  
  国家自然科学基金面上项目;National Natural Science Foundation of China(52074213);中煤西安设计工程有限责任公司项目China Coal Xi'an Design Engineering Co., Ltd.(XMSHT*KC-KY-SMGF-00-24-020)
- DOI：10.13296/j.1001-1609.hva.2026.08.008
  中图分类号：
- 收稿：2026-04-30，
  
  修回：2026-06-18，
  
  纸质出版：2026-08-16
- 稿件说明：
移动端阅览
张京娥, 程中杰, 刘宝. 基于注意力—门控循环—时间卷积网络的变压器油温预测[J]. 高压电器, 2026,62(8):58-66.

ZHANG Jing'e, CHENG Zhongjie, LIU Bao. Transformer Oil Temperature Prediction Based on Attention-gated Recurrent-temporal Covolutional Network[J]. High Voltage Apparatus, 2026, 62(8): 58-66.
张京娥, 程中杰, 刘宝. 基于注意力—门控循环—时间卷积网络的变压器油温预测[J]. 高压电器, 2026,62(8):58-66. DOI： 10.13296/j.1001-1609.hva.2026.08.008.

ZHANG Jing'e, CHENG Zhongjie, LIU Bao. Transformer Oil Temperature Prediction Based on Attention-gated Recurrent-temporal Covolutional Network[J]. High Voltage Apparatus, 2026, 62(8): 58-66. DOI： 10.13296/j.1001-1609.hva.2026.08.008.

摘要

顶层油温是评价电力变压器热特性的重要指标，准确预测顶层油温变化趋势对评估变压器的负载能力和及时发现潜在的内部热故障具有重要作用。针对顶层油温预

测中精度低、特征选择过程繁琐和信息使用错误的问题，提出了一种基于时间卷积网络（TCN）、注意力机制（attention mechanism）和门控循环单元（GRU）的变压器顶层油温预测方法（Attention-GRU-TCN）。首先，在卷积中引入TCN，保证预测过程中的因果性，减少未来数据对预测过程的干扰。其次，注意力机制的引入，赋予关键数据特征更高的权重，取代复杂耗时的数据筛选过程，提高预测精度。最后，在某公司与传统的变压器顶层油温预测算法（例如，RNN、LSTM、GRU、BiLSTM、Transformer等）进行实验实测。结果表明，与RNN模型、LSTM模型、GRU模型、BiLSTM模型、Transformer模型相比，Attention-GRU-TCN模型的平均绝对误差（

MAE

）分别下降23.69%、15.88%、7.77%、29.35%、14.51%；均方误差（

MSE

）分别下降45.79%、31.34%、12.31%、43.32%、38.85%；决定系数（

）上升6.0%、3.10%、0.79%、12.30%、4.57%。说明该方法对变压器顶层油温的预测精度更高，有助于为潜在热故障预警提供参考并分析变压器负载能力。

Abstract

Top oil temperature is an important indicator for evaluating thermal characteristics of power transformer. Accurately predicting the variation trend of the top oil temperature plays a crucial role in assessing the load capacity of the transformer and promptly identifying potential thermal faults. In view of such issues as low accuracy

cumbersome feature selection process

and incorrect use of information in top oil temperature prediction

a transformer top oil temperature prediction method(Attention-GRU-TCN)is proposed based on temporal convolutional network (TCN)

attention mechanism

and gated recurrent unit(GRU). First

TCN is introduced into the convolution to ensure causality in the prediction process and reduce the interference of future data on the prediction process. Then

the introduction of the attention mechanism assigns higher weights to key data features

replacing the complex and time-consuming data filtering process

thereby improving prediction accuracy. Finally

the proposed method is experimentally validated at a company against traditional top oil temperature prediction models(e.g.

RNN

LSTM

GRU

BiLSTM

andTransformer

ect).The results show that compared with the RNN model

LSTM model

GRU model

BiLSTM model and Transformer model

the Attention-GRU-TCN model achieves a decrease in mean absolute error(

MAE

)by 23.69%

15.88%

7.77%

29.35%

and 14.51% respectively

a decrease in mean squared error(

MSE

)by 45.79%

31.34%

12.31%

43.32%

and 38.85% respectively;and an increase in the coefficient of determination(

)by 6.0%

3.10%

0.79%

12.30%

and 4.57% respectively. It is shown that the method has higher prediction accuracy for transformer top oil temperature

which is conducive to providing a reference for early warning of potential thermal faults and analyzing the load capacity of transformers.

关键词

Keywords

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