| Popis: |
The deterioration of cable joints will lead to the increase of heat loss,and then lead to the rise of surface temperature of the joints.At the same time,the surface temperature is affected by many factors such as operating load,environmental temperature.In general,the relationship between deterioration degree and temperature data shows a non-linear distribution.For this reason,a prediction method based on improved sparrow search algorithm(ISSA) optimization for kernel extreme learning machine(KELM) is proposed to predict the insulation deterioration degree of cable joints.Firstly,based on the experimental validation of the multi-physical coupling model of cable joints,the surface temperature distribution data of cable joints at different deterioration levels,loads and ambient temperatures are obtained for building the training set,validation set and test set.Secondly,the sparrow search algorithm is optimized based on the idea of flight behavior in the bird swarm algorithm(BSA),which ensures global convergence without losing population diversity and effectively jumps out of local optimum.Then,ISSA algorithm is used to optimize the penalty coefficient C and the kernel function σ of KELM and the prediction model of insulation deterioration state is obtained.Research results show that the predictive effect of ISSA-KELM is much better than that of other models. |
