Determination of the level of degradation of generator stator bar insulation using a onedimensional convolutional neural network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F24%3A43972990" target="_blank" >RIV/49777513:23220/24:43972990 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10693881" target="_blank" >https://ieeexplore.ieee.org/document/10693881</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/Diagnostika61830.2024.10693881" target="_blank" >10.1109/Diagnostika61830.2024.10693881</a>
Alternative languages
Result language
angličtina
Original language name
Determination of the level of degradation of generator stator bar insulation using a onedimensional convolutional neural network
Original language description
This paper presents the results of an experiment to classify the levels of insulation degradation of generator stator bars using a one-dimensional convolutional neural network. The stator bars were subjected to increased electrical stress during the time until insulation breakdown. The bars were periodically injected with a specially designed reference signal during the stress application to generate a dataset for training the neural network. The injected signal was acquired and then subjected to pre-processing. The paper evaluates each pre-processing variant in terms of its effect on the performance of the classification algorithm. It provides the neural network structure and its optimal parameters to accomplish the task of determining the insulation degradation state.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2024 International Conference on Diagnostics in Electrical Engineering (Diagnostika) : /conference proceedings/
ISBN
979-8-3503-6149-0
ISSN
2464-7071
e-ISSN
2464-708X
Number of pages
4
Pages from-to
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Publisher name
IEEE
Place of publication
Piscataway
Event location
Pilsen, Czech Republic
Event date
Sep 3, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001345150300010