LAUSR

The onset process of corona discharge is naturally nonlinear and dynamic. The conventionally physical-based onset model and numerical computation of onset charge distribution are hampered by the computational power and given time. Here, in order to efficiently model this highly nonlinear dynamic process, a long short-term memory (LSTM) neural networks with attention mechanisms is proposed for accelerated charge density prediction under different atmospheric conditions, which adaptively choose charge-related input variables at each time step and hidden states relating to charge density all time steps. Our results demonstrate that this well trained model could make instant predictions with high accuracy under given target atmospheric conditions. Results show that the proposed model substantially reduces the computing time compared to physical-based methods. This work provides insights into applying LSTM neural networks to the charge density prediction of other discharge modes as well.