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Classification of Scalogram Signatures for Power Quality Disturbances Using Transfer Learning
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0002-3625-8578
Institute of Electrical and Energy Systems, Federal University of Itajuba, Itajuba, Brazil.
Institute of Electrical and Energy Systems, Federal University of Itajuba, Itajuba, Brazil.
2022 (English)In: 2022 20th International Conference on Harmonics & Quality of Power (ICHQP) Proceedings: “Power Quality in the Energy Transition”, IEEE, 2022Conference paper, Published paper (Refereed)
Abstract [en]

The electrical power systems have gone through a process of transformations that will remain characterized by a wide penetration of renewable sources, electronic devices, and computerization. In this context, Power Quality (PQ) is associated with several challenges for the sector, presenting new issues and new scenarios for old problems. Signal processing (SP) plays an essential role in PQ applications as a tool that helps measure, characterize, and visualize electrical grid disturbances. At the same time, artificial intelligence (AI) is becomming more and more useful to classification tasks regarding PQ disturbances . This work aims to employ a transfer learning methodology for PQ disturbances classification. Wavelet scalograms of the signal are created using CWT for feature extraction of time-frequency signatures. The 2-D images of this representation are used to train and test pre-trained CNN models’ performance. The work aims to contribute to PQ disturbances classification through innovative methods and assess the performance of different CNNs models that have a significant role in image classification. The performance of four network models is assessed: ResNet-18, VGG-19, Inception-v3, and ResNet-101. Discussion and consideration about the results provide evaluation of the methodology.

Place, publisher, year, edition, pages
IEEE, 2022.
Series
International Conference on Harmonics and Quality of Power, ISSN 1540-6008, E-ISSN 2164-0610
Keywords [en]
deep learning, power quality, signal processing, scalograms
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-92114DOI: 10.1109/ICHQP53011.2022.9808673ISI: 000844604500073Scopus ID: 2-s2.0-85133746223ISBN: 978-1-6654-1639-9 (electronic)OAI: oai:DiVA.org:ltu-92114DiVA, id: diva2:1681921
Conference
20th International Conference on Harmonics & Quality of Power (ICHQP 2022), Naples, Italy, May 29 - June 1, 2022
Note

Funder: FAPEMIG; CAPES (001); CNPq; INERGE

Available from: 2022-07-07 Created: 2022-07-07 Last updated: 2022-09-19Bibliographically approved

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De Souza Salles, Rafael

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