Establishment and Optimization of Stator Bar End Model Based on SHO-RBF

To establish the complex functional relationship between the stator bar end structure and the maximum electric field strength, and to optimize the anti-corona structure, an optimization model for the stator bar end based on the Seahorse Optimization algorithm—Radial Basis Function (SHO-RBF) neural network is proposed in this paper. The RBF neural network is employed to establish the complex relationship between the maximum electric field strength at the stator bar end and the anti-corona structure parameters. The SHO is introduced to find the optimal anti-corona structure at the stator bar end structure. A simulation model of the stator bar end is developed, and 30 sets of simulation data are collected for training and optimization purposes. The relationship between the stator bar end structure and the maximum electric field strength is established, and an optimized scheme comprising six groups of anti-corona structures is developed. The feasibility of the proposed design is validated through simulation calculations. Compared to manually adjusting parameters individually within the simulation model, this approach offers a significant advantage in terms of computational efficiency and speed.