[Oral Presentation]Temperature Prediction of Substation Distribution Cabinet Based on CNN-BiGRU Model with Attention Mechanism

Temperature Prediction of Substation Distribution Cabinet Based on CNN-BiGRU Model with Attention Mechanism
ID:8 Submission ID:14 View Protection:ATTENDEE Updated Time:2023-11-20 13:45:31 Hits:198 Oral Presentation

Start Time:2023-12-10 09:00 (Asia/Shanghai)

Duration:15min

Session:[S10] Electric Machine Design and control » [S10] Electric Machine Design and control

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Abstract
In order to solve the problem of weak generalization ability of existing temperature prediction models when adapted to multiple devices in substation distribution cabinets, this paper proposes a CNN-BiGRU temperature prediction model with an attention mechanism. First, data preprocessing is carried out using normalization and outlier removal. Second, the BiGRU layer with attention mechanism is introduced in the hidden layer to filter the non-important information, and the residual-connected CNN architecture layer is utilized to avoid the problem of gradient vanishing. Finally, the effectiveness of the proposed model is verified by combining the actual temperature data of four distribution cabinets in a substation. The results show that the temperature prediction model proposed in this paper exhibits higher temperature prediction accuracy compared to LSTM, GRU, CNN-LSTM, CNN-GRU models.
 
Keywords
Substation distribution cabinet; Temperature prediction; CNN-BiGRU model; Attention mechanism; Residual network
Speaker
Junchen Lu
School of Big Health and Intelligent Engineering,Chengdu Medical College

Submission Author
Ping Hu School of Big Health and Intelligent Engineering,Chengdu Medical College
Junchen Lu School of Big Health and Intelligent Engineering,Chengdu Medical College
Yuan Cui School of Big Health and Intelligent Engineering,Chengdu Medical College
Bo Hu School of Big Health and Intelligent Engineering,Chengdu Medical College
Fan Liang Tangshan Research Institute,Southwest Jiaotong University
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