中国电机工程学报2024，Vol.44Issue(z1)：318-331,14.DOI:10.13334/j.0258-8013.pcsee.231239
基于自监督预训练与时序注意力机制的变压器顶层油温预测
Transformer Top Oil Temperature Prediction Based on Self-supervised Pre-training and Time-series Attention Mechanism
李启明 1李彬 1刘浩 2甘津瑞 2石富岭 1卢卫疆 2杨春萍1
作者信息
- 1. 华北电力大学电气与电子工程学院,北京市 昌平区 102206
- 2. 国网智能电网研究院有限公司,北京市 昌平区 102209
- 折叠
摘要
Abstract
Transformer fault early warning relies on more accurate and reliable prediction of transformer top oil temperature.The self-supervised pre-training method is used to transform the oil temperature prediction task in the forecasting model training process into the oil temperature reconstruction task,which generalizes the model training method and improves the model's ability to extract historical oil temperature situation information.A dual-channel pre-trained time-series attention network(DPAnet)model is proposed.The model uses a time-series attention mechanism and a deep neural network with trend cycle branches,respectively targeting the trend laws of oil temperature data.The periodic law realizes modeling at the single time step level,thus enhancing the predictive ability on multiple time steps.The example analysis shows that in the short-term prediction scenario of 1～72 h with hourly granularity,the average prediction loss of the top oil temperature prediction model proposed in this paper is 1.847,and the average certainty coefficient is 0.862,which improves the prediction compared with other models.The model has strong generalization ability and robustness,and effectively supports the analysis of the change trend of oil temperature on the top layer of the transformer.关键词
顶层油温/自监督预训练/时序注意力/深度神经网络Key words
top oil temperature/self-supervised pre-training/time self-attention/deep neural network分类
信息技术与安全科学引用本文复制引用
李启明,李彬,刘浩,甘津瑞,石富岭,卢卫疆,杨春萍..基于自监督预训练与时序注意力机制的变压器顶层油温预测[J].中国电机工程学报,2024,44(z1):318-331,14.
