Precise forecasting of the thermal parameters is a critical factor for the safe operation and fault incipient warning of the ultra-high voltage (UHV) transformers. In this work, a novel multi-step forecasting method based on the long-and short-term time-series network (LSTNet) with the conditional mutual information (CMI) is proposed for the UHV transformer. To improve the computational efficiency and eliminate the redundancy, the CMI-based feature selection algorithm is applied to analyse the correlation between the original monitoring parameters and construct the optimal feature subset. LSTNet, which is composed of a convolutional layer, recurrent layer and recurrent-skip layer, is utilized to capture both the short-term nonlinear characteristics and the longterm periodic characteristics. The LSTNet model is established to forecast the variation tendency of the oil and winding temperatures for different locations in the UHV transformer. The results show that the proposed method significantly enhances the accuracy in both one-step and multi-step thermal parameters forecasting and achieves better performance in terms of the RMSE and MAE compared with other existing methods. K E Y W O R D Sconditional mutual information (CMI), forecasting method, long-and short-term time-series network (LSTNet), top oil temperature, UHV transformers, winding temperatureThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

In order to improve the prediction accuracy of ultra-high voltage (UHV) transformer winding temperature, a hybrid model based on convolutional neural network (CNN) and long short-term memory (LSTM) network is proposed. This method uses a large number of historical monitoring data, including data of winding temperature, top oil temperature, dissolved gas in oil, environmental data and so on. Combined with the characteristics of CNN and LSTM networks, CNN convolution layer and pooling layer are used to extract relevant data information to generate feature vectors, then the feature vectors are used as input data to predict winding temperature through LSTM network. The example results show that this method is of high prediction accuracy, its prediction accuracy is better than that of single LSTM model, which can effectively improve the prediction accuracy of UHV transformer winding temperature.

No abstract

The top oil temperature in ultra-high voltage (UHV) reactors has attracted enormous interest due to its wide applications in fault diagnosis and insulation evaluation. In this work, the precise prediction method based on the Seq2Seq module with the convolutional block attention mechanism is proposed for the UHV reactor. To reduce the influence of vibratility and improve computational efficiency, a combination of the encoding layer and decoding layer named Seq2Seq is performed to reconstruct the complex raw data. The convolutional block attention mechanism (CBAM), composed of spatial attention and channel attention, is utilized to maximize the use of information in data. The Seq2Seq-CBAM is established to forecast the variation tendency of the oil temperatures in the UHV reactor. The experimental results show that the proposed method achieves high prediction accuracy for the top oil temperature in both single-step and multi-step.