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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.

The development of nonfullerene acceptors (NFAs) has led to dramatic improvements in the device efficiencies of organic photovoltaic (OPV) cells. To date it is, however, still unclear how those laboratory‐scale efficiencies transfer to commercial modules, and how stable these devices will be when processed via industrially compatible methods. Herein, the degradation behavior of lab‐scale and scalable OPV devices using similar nonfullerene‐based active layers is assessed. It is demonstrated that the scalable NFA OPV exhibits completely reversible degradation when assessed in ISOS‐O‐1 outdoor conditions, which is in contrast to the laboratory‐scale devices assessed via the indoor ISOS‐L‐2 protocol. Results from transient photovoltage (TPV) indicate the presence of charge trap formation, and a number of potential mechanisms are proposed for the selective occurrence of this in laboratory‐scale devices tested in ISOS‐L laboratory conditions—ultimately concluding that it has its origins in the different device architectures used. The study points at the risk of assessing active layer stability from laboratory‐scale devices and degradation studies alone and highlights the importance of using a diverse range of testing conditions and ISOS protocols for such assessment.

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Given the non-linearity of changes in the dielectric and mechanical properties of insulation, it is difficult to find neither a theoretical nor an experimental

-This work concerns the modeling of a photovoltaic cell and the prediction of the sensitivity of electrical parameters (current, power) of the six types of photovoltaic cells based on voltage applied between terminals using one of the best-known artificial intelligence technique, which is the Artificial Neural Networks. The results of the modeling and prediction have been shown and then compared between them. NEWFF learning algorithm was used with specified number of iteration that gave the best results. The error was calculated in all cases to check the accuracy of the used method.