The arithmetic sequences optimised by genetic algorithms have been applied in the lifetime estimation and diagnosis of cross‐linked polyethylene (XLPE) high‐voltage insulation under thermal ageing. To know an idea of the cable capacity to work without failure, it is necessary to predict the future state of the electrical insulation. If the estimation is very good, the authors can start to schedule maintenance tasks of electrical system and find preventive solutions in early time. A large amount of money can be saved if they take appropriate actions. The developed model gives results in good agreement with the experimental results, with an acceptable error margin. The authors also applied the same models in the diagnosis of the high‐voltage insulation to plan the preventive maintenance actions. The decision is taken from the carried out previous experimental measurements of XLPE properties. The developed approaches are able to make the diagnosis and the classification of the insulation state. The results of modelling, prediction and diagnosis presented in this work demonstrate the effectiveness of the used method.

The aim of this research is to predict the mechanical properties along with the behaviors of standard insulating paper used in power transformers under thermal aging. This is conducted by applying an artificial neural network (ANN) trained with a multiple regression model and a particle swarm optimization (MR-PSO) model. The aging of the paper insulation is monitored directly by the tensile strength and the degree of polymerization of the solid insulation and indirectly by chemical markers using 2-furfuraldehyde compound content in oil (2-FAL). A mathematical model is then developed to simulate the mechanical properties (degree of polymerization (DP_V) and tensile index (Tidx)) of the aged insulation paper. First, the datasets obtained from experimental results are used to create the MR model, and then the optimizer method PSO is used to optimize its coefficients in order to improve the MR model. Then, an ANN method is trained using the MR-PSO to create a nonlinear correlation between the DP_V and the time, temperature, and 2-FAL values. The acquired results are assessed and compared with the experimental data. The model presents almost the same behavior. In particular, it has the capability to accurately simulate the nonlinear property behavior of insulation under thermal aging with an acceptable margin of error. Since the life expectancy of power transformers is directly related to that of the insulating paper, the proposed model can be useful to maintenance planners.