Szczegóły publikacji
Opis bibliograficzny
Classification of partial discharge images using deep convolutional neural networks / Marek FLORKOWSKI // Energies [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1996-1073. — 2020 — vol. 13 iss. 20 art. no. 5496, s. 1–17. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 14–17, Abstr. — Publikacja dostępna online od: 2020-10-20
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 130973 |
|---|---|
| Data dodania do BaDAP | 2020-11-12 |
| Tekst źródłowy | URL |
| DOI | 10.3390/en13205496 |
| Rok publikacji | 2020 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Energies |
Abstract
Artificial intelligence-based solutions and applications have great potential in various fields of electrical power engineering. The problem of the electrical reliability of power equipment directly refers to the immunity of high-voltage (HV) insulation systems to operating stresses, overvoltages and other stresses—in particular, those involving strong electric fields. Therefore, tracing material degradation processes in insulation systems requires dedicated diagnostics; one of the most reliable quality indicators of high-voltage insulation systems is partial discharge (PD) measurement. In this paper, an example of the application of a neural network to partial discharge images is presented, which is based on the convolutional neural network (CNN) architecture, and used to recognize the stages of the aging of high-voltage electrical insulation based on PD images. Partial discharge images refer to phase-resolved patterns revealing various discharge stages and forms. The test specimens were aged under high electric stress, and the measurement results were saved continuously within a predefined time period. The four distinguishable classes of the electrical insulation degradation process were defined, mimicking the changes that occurred within the electrical insulation in the specimens (i.e., start, middle, end and noise/disturbance), with the goal of properly recognizing these stages in the untrained image samples. The results reflect the exemplary performance of the CNN and its resilience to manipulations of the network architecture and values of the hyperparameters. Convolutional neural networks seem to be a promising component of future autonomous PD expert systems.
