Research on improved LSTM power transformer oil temperature prediction based on spatio-temporal fusion

Yidong Zhang; Xinghua Yang

doi:10.1117/12.3035438

16 August 2024 Research on improved LSTM power transformer oil temperature prediction based on spatio-temporal fusion

Yidong Zhang, Xinghua Yang

Author Affiliations +

Proceedings Volume 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024); 132302A (2024) https://doi.org/10.1117/12.3035438
Event: Third International Conference on Machine Vision, Automatic Identification and Detection, 2024, Kunming, China

Abstract

This study aims to enhance the prediction of power transformer oil temperature time series by proposing a model that incorporates CNN, BiLSTM, and attention network structures. To address the limitation of unidirectional information transfer in the LSTM model, we introduce CNN for feature extraction, use the attention mechanism to enhance the learning effect, and employ the TTAO optimization algorithm to optimize the model parameters. The experimental results indicate that the TTAO-CNN-BiLSTM-Attention model achieves optimal performance on the MAE, R-squared, and Time metrics, slightly lagging behind the BiLSTM-Attention model on MSE and RMSE. However, overall, the established TTAO-CNN-BiLSTM-Attention model demonstrates good effectiveness. Compared to the LSTM model, there are reductions of 34.5%, 54.6%, 32.6%, and 12.1% in MAE, MSE, RMSE, and Time respectively, with a 14.3% increase in R-squared. Additionally, the R-square value is higher than that of all other models, suggesting a wide range of potential applications and practical value.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yidong Zhang and Xinghua Yang "Research on improved LSTM power transformer oil temperature prediction based on spatio-temporal fusion", Proc. SPIE 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024), 132302A (16 August 2024); https://doi.org/10.1117/12.3035438

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