基于TCN-Crossformer-BiLSTM的配电变压器重过载预测与供电恢复研究OA

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.