Abstract: Using the time series data generated during the operation of substation electrical equipment,a predictive model can be constructed for its future operating state,thereby detecting abnormal data in advance,eliminating hidden faults,and improving stability and reliable operation ability.The Transformer model is an emerging sequential data processing model that has advantages when dealing with longer sequences and can meet the forward-looking needs of malfunction alarm.However,the model structure of Transformer makes it difficult to be directly applied to malfunction alarm tasks due to its high computational complexity and space occupancy.Therefore,a Transformer equipment malfunction alarm method based on time series prediction is proposed,which improves the Transformer model to achieve modeling of equipment operation data.The model uses a dual-tower encoder structure to extract features of sequences in both frequency and time domains,and performs multi-dimensional data fusion on time feature data and space feature data to extract more detailed information.Secondly,sparse attention mechanism is used instead of standard attention mechanism to reduce the computational complexity and space occupancy rate of Transformer and meet the needs of real-time warning.The superiority of the proposed model and the necessity of the improved module are demonstrated by experiments on ETT transformer equipment dataset.Compared with other methods,the proposed model achieves optimal MSE and MAE indices in most prediction tasks,especially in long sequence prediction tasks,and has faster prediction speed.

Key words: Equipment malfunction alarm, Time series forecasting, Deep learning, Transformer