The classification of distinct problems of insulators in the distribution networks is a task that requires operator's experience. The application of techniques to automate the inspection of electrical systems with the objective of detecting faults in insulators have shown to be reasonable alternatives to improve reliability in power grid. Based on the development of an experimental setup, signals were acquired considering three distinct faults in insulators. In this case, 13.8kV (RMS) was applied in drilled, contaminated, and good insulators considering an ultrasound detector connected to a computer. In the sequence, a multiclass classification method was proposed considering the ensemble of classifiers. The method considered the association of five distinct techniques, Bottom-Up segmentation, Wavelet Energy Coefficient (WEC), Principal Component Analysis (PCA), and Particle Swarm Optimization (PSO) associated to Ensemble Extreme Learning Machine (EN-ELM). Named as Optimized Ensemble Extreme Learning Machine (OEN-ELM), the present approach outperforms the original EN-ELM method. Finally, results showed significant increase in robustness and faster training procedure when compared to classical approaches.

The surface contamination of electrical insulators can increase the electrical conductivity of these components, which may lead to faults in the electrical power system. During inspections, ultrasound equipment is employed to detect defective insulators or those that may cause failures within a certain period. Assuming that the signal collected by the ultrasound device can be processed and used for both the detection of defective insulators and prediction of failures, this study starts by presenting an experimental procedure considering a contaminated insulator removed from the distribution line for data acquisition. Based on the obtained data set, an offline time series forecasting approach with an Adaptive Neuro-Fuzzy Inference System (ANFIS) was conducted. To improve the time series forecasting performance and to reduce the noise, Wavelet Packets Transform (WPT) was associated to the ANFIS model. Once the ANFIS model associated with WPT has distinct parameters to be adjusted, a complete evaluation concerning different model configurations was conducted. In this case, three inference system structures were evaluated: grid partition, fuzzy c-means clustering, and subtractive clustering. A performance analysis focusing on computational effort and the coefficient of determination provided additional parameter configurations for the model. Taking into account both parametrical and statistical analysis, the Wavelet Neuro-Fuzzy System with fuzzy c-means showed that it is possible to achieve impressive accuracy, even when compared to classical approaches, in the prediction of electrical insulators conditions.