Abstract
Insulators are vital protection and isolation barriers used in power transmission systems. To prevent unexpected failures caused by severe weather conditions, it is important to develop intelligent insulator fault prognosis systems. To this end, this study has focused to examine and analyze normal (clean) and contaminated (wet) insulators through a new technique that can act as a catalyst for possible future solutions to the data-driven classification of insulator surface conditions. In particular, a novel stacked ensemble learning based on six pretrained deep convolutional neural networks (CNNs), which are used as level-0 generalizers in the stacked structure, is proposed. As the level-1 generalizer that is used in the stacked structure, a majority vote is considered among selected subsets of level-0 generalizers. In other words, training the level-1 generalizer is equivalent to selecting the best subset of pretrained CNNs for classifying a clean insulator surface from those sprayed with water droplets. Since a wetted insulator surface can enhance the electric field intensity and may lead to flashover when combined with other contaminants (dust, soil, cement, etc.), water droplets are considered a type of contamination in our study. Considering a tradeoff between performance and model complexity in training the level-1 generalizer points to two combinations of pretrained CNNs, namely, EfficientNetB2-ResNet50-Xception, and MobileNet-DenseNet121-Xception. Our empirical results show that (i) both combinations lead to better performance when compared with individual pretrained models, and (ii) the latter combination leads to a considerably lower complexity (~39% less parameters) at the expense of ~9% reduction in accuracy.