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Analysis of institutional authors

Mor, A RodrigoAuthor

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February 3, 2025
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Partial discharges and noise classification under HVDC using unsupervised and semi-supervised learning

Publicated to: INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS. 121 106129- - 2020-10-01 121(), DOI: 10.1016/j.ijepes.2020.106129

Authors:

Morette, N; Heredia, L C Castro; Ditchi, Thierry; Mor, A Rodrigo; Oussar, Y
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Affiliations

Delft Univ Technol, Dept Elect Sustainable Energy, Delft, Netherlands - Author
Sorbonne Univ, PSL Res Univ, LPEM, ESPCI Paris,CNRS, Paris, France - Author

Abstract

This paper tackles the problem of the classification of partial discharge (PD) and noise signals by applying unsupervised and semi-supervised learning methods. The first step in the proposed methodology is to prepare a set of classification features from the statistical moments of the distribution of the Wavelet detail coefficients extracted from a dataset of signals acquired from a test cell under 40 kVDC. In a second step, an unsupervised learning framework that implements the k-means algorithm is applied to reduce the dimensionality of this initial feature set. The Silhouette index is used to evaluate the number of natural clusters in the dataset while the Dunn index is used to determine which subset of features produces the best clustering quality. Since the unsupervised learning does not provide any method for result validation, then the third step in the methodology of this paper consists of applying a semi-supervised learning framework that implements Transductive Support-Vector Machines. The labeling of the test set that is required in this framework for the result validation is carried out by visual checking of the signal waveforms assisted by GUI tools such as the software PDflex. The results using this methodology showed a high classification accuracy and proved that both learning frameworks can be combined to optimize the selection of classification features.
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Keywords

ChargDunn indexEnergyHvdK-meansPartial dischargesSemi-supervised learningSeparationTransductive svmsTransform

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2020, it was in position 45/273, thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering, Electrical & Electronic.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.03. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-06, the following number of citations:

  • WoS: 19
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-06:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 30.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 30 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 3.
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: France; Netherlands.

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