关键词: 高压电缆, 温度预测, 多尺度, patch, 卷积交互

Abstract: Overheating of high-voltage cables can lead to insulation thermal breakdown, compromising the stability of power grid. However, existing research primarily focuses on traditional prediction models, which often fail to capture the complexity and dynamic characteristics of temperature data. To address this limitation, a novel cable temperature prediction model based on multi-scale Patch and convolutional interaction (MSP-CI) was proposed. Initially, input dimension was reduced using a channel resampling method, and a multi-scale patch branch structure was designed to decouple complex time series. Then, macroscopic information from coarse-grained patches and microscopic information from fine-grained patches were extracted through a combination of sequence decomposition and convolution interaction strategy. Finally, an attention fusion module was introduced to dynamically balance the weights of macroscopic and microscopic information, yielding the final prediction results. Experimental results on real high-voltage cable temperature datasets demonstrate that, compared to baseline models such as TimeMixer, PatchTST, and MSGNet, MSP-CI achieves an average reduction of 6.97% to 35.79% in mean squared error (MSE) and 6.36% to 42.84% in mean absolute error (MAE). These findings indicate that MSP-CI effectively enhances cable temperature prediction accuracy, providing a reliable basis for power dispatching operations.

Key words: high-voltage cable, temperature prediction, multi-scale, patch, convolutional interaction