Neural networks and fuzzy logic approaches to predict mechanical properties of XLPE insulation cables under thermal aging

Abstract

The widespread use of cross-linked polyethylene (XLPE) as insulation in the manufacturing of medium- and high-voltage cables may be attributed to its outstanding mechanical and electrical properties. However, it is well known that degradation under service conditions is the major problem in the use of XLPE as cable insulation. Laboratory investigations of the insulations aging are time-consuming and cost-effective. To avoid such costs, we have developed two models which are based on artificial neural networks (ANNs) and fuzzy logic (FL) to predict the insulation properties under thermal aging. The proposed ANN is a supervised one based on radial basis function Gaussian and trained by random optimization method algorithm. The FL model is based on the use of fuzzy inference system. Both models are used to predict the mechanical properties of thermally aged XLPE. The obtained results are evaluated and compared to the experimental data in depth by using many statistical parameters. It is concluded that both models give practically the same prediction quality. In particular, they have ability to reproduce the nonlinear behavior of the insulation properties under thermal aging within acceptable error. Furthermore, our ANN and FL models can be used in the generalization phase where the prediction of the future state (not reached experimentally) of the insulation is made possible. Additionally, costs and time could be reduced.