Evaluation of transfer learning approaches for partial discharge classification in hydrogenerators

Novel Self-Organizing Probability Maps Applied to Classification of Concurrent Partial Discharges from Online Hydro-Generators

“…The obtained results indicates that the developed method is working as expected when classifying single-or multiple-class samples. Note that when test samples are used, an average accuracy rate of 88.21% was achieved, which is comparable to what is seen in the literature for single-class sample classifiers [8][9][10][11][12].…”

“…Araújo et al [8] implemented a Multilayer Perceptron (MLP) algorithm, employing normalized histograms as input features and achieving a single PD source classification accuracy of 94.4%. Lopes et al [9] utilized Convolutional Neural Networks (CNNs) with real-world hydro-generator data with histograms as input, resulting in an accuracy of 89.44% for single-source PD classification. Pardauil et al [10] combined k-means and Random Forest (RF) algorithms with data mining and clustering, using histograms as input, and achieved an accuracy of 99% for classifying single PD sources.…”

“…SCL achieves a classification accuracy over 96% for single-class PRPDs. Note that in [8][9][10][11][12][13], PRPD maps with only one well-defined PD pattern each were considered and, therefore, accuracy rates tended to be around 90%.…”

“…These complexities highlight the limitations of traditional PD diagnostic methods relying on human expertise and underscore the need for automated and robust solutions. Prior research has explored various machine learning algorithms for PD classification, including Kohonen neural networks [7], Multilayer Perceptrons [8], and Convolutional Neural Networks [9]. However, these methods predominantly focus on single-class PD patterns, limiting their applicability in real-world scenarios where multiple PD sources are often present.…”