The contact erosion during the arc duration affects the attachment position of arc roots, directly determining the arc motion speed. In this paper, a two-way coupling model is proposed to study the interaction between contact edge erosion and arc root stagnation-transition during direct current air arc interruption. A two-dimensional transient model for contact erosion and droplet splashing is established using an improved level-set method, combined with the magnetohydrodynamic model of arc plasma and contact energy transfer, considering the mass transfer, heat transfer, and fluid flow during the arc root stagnation-transition comprehensively. The model's accuracy is verified through experiments. The results show that the arc root stagnation forms molten pool or even droplet splashing. Vortices below the moving contact interfere favorably with arc root transition. However, contact deformation induced by erosion affects heat dissipation at the arc root, weakens the vortices' aerodynamic performance, hastens vortex bubble rupture, and delays arc root transition. Increasing breaking speed and arc-blowing magnetic induction intensity shortens stagnation time and reduces edge erosion. However, the breaking speed more than 5 m/s or the magnetic induction intensity more than 50 mT shows no significant improvement on the arc root transition speed, while erosion intensifies when magnetic induction intensity exceeds 50 mT.

To tackle the challenges of low prediction accuracy caused by single-feature modeling, and the hidden state of the neural network easily loses some information of the long time series, a method for predicting the remaining electrical life of AC contactor using a convolutional autoencoder-bidirectional gated recurrent unit-attention (CAE-BiGRU-Attention) was proposed in this work. Firstly, the feature parameters were extracted from the AC contactor full-life test, and an optimal feature subset was selected using neighborhood component analysis and Spearman rank correlation coefficient to characterize the degradation state of electrical life effectively. Then, the deep information of the optimal feature subset was extracted using CAE. Finally, the remaining electrical life of the AC contactor was treated as a long time series problem and predicted in time series by BiGRU-Attention accurately. The case analysis demonstrates that the model has better prediction accuracy than recurrent neural network (RNN), long short-term memory (LSTM), GRU, BiGRU and CAE-BiGRU models, with an average effective accuracy of 97.12%. This effectively demonstrates the model’s feasibility to accurately predict temporal sequences in the remaining electrical life prediction of electrical equipment.

AC contactors are used frequently in various low-voltage control lines, so remaining-life prediction for them can significantly improve the operational reliability of power control systems. To address the problem that the existing AC contactor remaining-life prediction methods do not make full use of the correlation between previous and later states in the degradation process, a CNN-GRU (convolutional neural network-gated recurrent unit) method for AC-contactor remaining-life prediction is proposed. Firstly, the entire cycle of an AC contactor’s degradation data is obtained through a whole-life test, from which the characteristic parameters that effectively reflect the operating states of the contactor are extracted; secondly, neighborhood component analysis (NCA) and maximal information coefficient (MIC) are used to eliminate the redundant information of multidimensional parameters in order to select the optimal feature subset; and then, CNN is used to compress the feature dimension and mine the regular information between the features, so as to extract the effective feature vectors; finally, taking the AC contactor remaining electrical life as a long time sequence issue, time-series accurate prediction is performed using GRU. It is verified that this model is better than RNN (recurrent neural network), LSTM (long short-term memory) and GRU models in prediction, with an effective accuracy of 96.63%, which effectively supports the feasibility of time-series prediction in the field of the remaining-life prediction of electrical devices.