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- W4226543103 abstract "Transmission line icing has a major impact on power grid security and stability, which affects human life, directly. Accurate transmission line tension prediction can duly avoid power grid suffering from serious line ice coating. However, transmission line tension is susceptible to many aspects, e.g. meteorological factors (temperature, wind speed, humidity, etc.) and transmission line attributions (tower model, voltage level, etc.), which makes it difficult to predict. In this paper, we propose a multi-source information fusion method with Gated Temporal Convolutional Network (GTCN) for Transmission line icing tension prediction. Concretely, a multi-source information fusion module is designed for modeling of historical line tension, metrological factors and transmission line attributions. To capture the temporal dependency with various ranges and long sequences, a gated dilated inception module is further proposed. Besides, residual connection and skip connection are used to alleviate gradient vanishing and standardize information. Furthermore, we conduct experiments on collected multi-source data. Experimental results show that GTCN outperforms the prevalent time series prediction baseline methods." @default.
- W4226543103 created "2022-05-05" @default.
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- W4226543103 date "2021-11-26" @default.
- W4226543103 modified "2023-10-12" @default.
- W4226543103 title "Multi-source Information Fusion with Gated Temporal Convolutional Network for Transmission Line Icing Tension Prediction" @default.
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- W4226543103 doi "https://doi.org/10.1109/iske54062.2021.9755420" @default.
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