Solving the Inverse Design Problem of Electrical Fuse With Machine Learning

Electric fuses are protection devices with a long history. They are widely used in both high voltage and low voltage power systems. The pre-arc I-t (current versus time) characteristic is an important parameter of electric fuse. The traditional method of obtaining this parameter is designing the electrical fuse by experience, then conducting experiments to obtain the I-t characteristic. However, customers request specific of I-t characters first, and then the engineers have to design electric fuses based on the requirements, this is an inverse design problem of electric fuses. There is no research in this area yet. The objective of this paper is to propose a new method to solve the inverse design problem of electrical fuse with machine learning. The method contains two steps. First, finite element analysis is used to obtain training samples for machine learning. A total of 252 samples (pre-arc current-time result) are obtained by simulations. Second, an elastic network is used to solve the probabilistic inference model by utilizing 217 samples from the total of 252. The other 35 samples are used to evaluate inverse design problem solution results. The results show that this method can accurately predict the design parameters of the restricted zone of fuse element, including the thickness (T
₁
) and radius (R
₁
) of restricted zone. The average relative errors value of T
₁
and R
₁
are 15.7% and 3.4%, respectively. Increasing the penalty factor and elastic error decreases the relative error. By changing the penalty error to 0.8, the average relative errors value of T
₁
and R
₁
are reduced by 14.1% and 1.7%, respectively. By changing the elastic error to 0.8, the average relative errors value of T
₁
and R
₁
are reduced by 13.1% and 2.1%, respectively. The research results provide a new solution for the electrical fuse design problem.