Abstract ZnO varistors are widely used in the field of lightning protection. After being struck by lightning, ZnO varistors absorb heat, which can lead to temperature rise and even damage, posing a serious safety threat to electrical safety. It is of great significance to predict the temperature rise of ZnO varistors.In this paper, multiple lightning tests were carried out, and the influence laws of the microscopic characteristics of ZnO varistors on their impulse tolerance and temperature rise were analyzed. Utilize the BP neural network method and by integrating the microscopic parameters such as grain size, uniformity, and grain boundary layer thickness of ZnO varistors, a multi-pulse lightning impulse temperature rise prediction model was constructed.Experimental verification shows that this model has high prediction accuracy, reveals the complex relationship between microscopic parameters and temperature rise, and provides new ideas and methods for the optimal design of ZnO varistors and the reasonable configuration of lightning protection systems.
Li et al. (Thu,) studied this question.