Dimensionality Reduction and Clustering Strategies for Label Propagation in Partial Discharge Data Sets

Ronaldo F. Zampolo (), Frederico H. R. Lopes, Rodrigo M. S. de Oliveira (), Martim F. Fernandes and Victor Dmitriev
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Ronaldo F. Zampolo: Institute of Technology (ITEC), Federal University of Pará (UFPA), Belém 66075-110, PA, Brazil
Frederico H. R. Lopes: Institute of Technology (ITEC), Federal University of Pará (UFPA), Belém 66075-110, PA, Brazil
Rodrigo M. S. de Oliveira: Institute of Technology (ITEC), Federal University of Pará (UFPA), Belém 66075-110, PA, Brazil
Martim F. Fernandes: Electrical Engineering Department, State University of Londrina, Londrina 86057-970, PR, Brazil
Victor Dmitriev: Institute of Technology (ITEC), Federal University of Pará (UFPA), Belém 66075-110, PA, Brazil

Energies, 2024, vol. 17, issue 23, 1-18

Abstract: Deep learning approaches have been successfully applied to perform automatic classification of phase-resolved partial discharge (PRPD) diagrams. Under the supervised learning paradigm, however, the performance of classifiers strongly depends on the availability of large and previously labeled data sets. Labeling is an intensive and time-consuming labor, typically involving the manual annotation of a large number of data examples by an expert. In this work, we propose a label propagation algorithm applied to PRPD data sets, aiming to reduce the time necessary to manually label PRPDs. Our basic pipeline is composed of three phases: pre-processing, dimensionality reduction procedures, and clustering. Different configurations of the basic pipeline are tested by using PRPDs obtained from online measurements in hydrogenerators. The performance of each configuration is assessed by using the Silhouette, Caliński–Harabasz, and Davies–Bouldin scores. The clustering of the best three configurations is compared with annotated PRPDs by using the Fowlkes-Mallows index. Results suggest our strategy can substantially decrease the time for manual labeling.

Keywords: label propagation; Kernel-PCA; PaCMAP; k-means++; clustering (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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