内蒙古电力技术2026，Vol.44Issue(3)：34-44,11.DOI:10.19929/j.cnki.nmgdljs.2026.0030
基于TCN-Crossformer-BiLSTM的配电变压器重过载预测与供电恢复研究
Research on Severe Overload Prediction and Power Supply Restoration for Distribution Transformer Based on TCN-Crossformer-BiLSTM
摘要
Abstract
To address the issue of rapid load surges in rural distribution grids during special periods such as the Spring Festival,which frequently lead to severe overload of distribution transformer,this paper proposes a research method for predicting severe overload of distribution transformer and restoring power supply based on a temporal convolutional network(TCN)-Crossformer-bi-directional long-short-term memory(BiLSTM)architecture.Firstly,a short-term load forecasting model based on TCN-Crossformer-BiLSTM is constructed.TCN extracts local time features while preserving causality.Crossformer characterizes global dependencies among external factors such as weather,dates,and holidays through cross-dimensional attention mechanisms.BiLSTM further enhances bidirectional memory and trend-fitting capabilities of sequences,thereby achieving high-precision modeling of complex non-stationary loads.Secondly,based on the predicting results and load factor discrimination criteria,severe overload risks of distribution transformer are identified,and an optimization model for power supply restoration is established with the objective of minimizing the maximum load factor during heavy and overload periods.Finally,the model is optimized and solved by combining butterfly optimization algorithm(BOA)and particle swarm optimization(PSO)approach.The case study results demonstrate that the proposed model effectively enhances the ability of load trend capture and the accuracy of perception of distribution transformer heavy overload risks.关键词
配电变压器/重过载/负荷预测/时间卷积网络/Crossformer/双向长短期记忆网络/供电恢复Key words
distribution transformer/severe overload/load forecasting/time convolutional network/Crossformer/bi-directional long short-term memory(BiLSTM)/power supply restoration分类
信息技术与安全科学引用本文复制引用
刘璐瑶..基于TCN-Crossformer-BiLSTM的配电变压器重过载预测与供电恢复研究[J].内蒙古电力技术,2026,44(3):34-44,11.基金项目
国家自然科学基金"计及源荷双端功率不确定性的电力系统暂态稳定约束最优潮流研究"(52407118) （52407118）
广东电网有限责任公司韶关供电局科技项目"基于风光储多能互补的解决配变短期重过载关键技术研究"(030200KC24070031) （030200KC24070031）
