{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T01:56:53Z","timestamp":1779328613407,"version":"3.51.4"},"reference-count":50,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,18]],"date-time":"2024-02-18T00:00:00Z","timestamp":1708214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this work, an exhaustive analysis of the partial discharges that originate in the bubbles present in dielectric mineral oils is carried out. To achieve this, a low-cost, high-resolution CMOS image sensor is used. Partial discharge measurements using that image sensor are validated by a standard electrical detection system that uses a discharge capacitor. In order to accurately identify the images corresponding to partial discharges, a convolutional neural network is trained using a large set of images captured by the image sensor. An image classification model is also developed using deep learning with a convolutional network based on a TensorFlow and Keras model. The classification results of the experiments show that the accuracy achieved by our model is around 95% on the validation set and 82% on the test set. As a result of this work, a non-destructive diagnosis method has been developed that is based on the use of an image sensor and the design of a convolutional neural network. This approach allows us to obtain information about the state of mineral oils before breakdown occurs, providing a valuable tool for the evaluation and maintenance of these dielectric oils.<\/jats:p>","DOI":"10.3390\/s24041317","type":"journal-article","created":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T03:18:38Z","timestamp":1708312718000},"page":"1317","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Characterization of Partial Discharges in Dielectric Oils Using High-Resolution CMOS Image Sensor and Convolutional Neural Networks"],"prefix":"10.3390","volume":"24","author":[{"given":"Jos\u00e9 Miguel","family":"Monz\u00f3n-Verona","sequence":"first","affiliation":[{"name":"Electrical Engineering Department (DIE), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain"},{"name":"Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pablo","family":"Gonz\u00e1lez-Dom\u00ednguez","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department (DIE), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain"},{"name":"Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Santiago","family":"Garc\u00eda-Alonso","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering and Automatics (DIEA), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"51382","DOI":"10.1109\/ACCESS.2023.3279355","article-title":"Review on Partial Discharge Diagnostic Techniques for High Voltage Equipment in Power Systems","volume":"11","author":"Hussain","year":"2023","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11698","DOI":"10.1109\/JSEN.2022.3172173","article-title":"Study of ultra high frequency measurement techniques for online monitoring of partial discharges in high voltage systems","volume":"22","author":"Ghanakota","year":"2022","journal-title":"IEEE Sens. 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