Determination of the level of degradation of generator stator bar insulation using a onedimensional convolutional neural network

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Determination of the level of degradation of generator stator bar insulation using a onedimensional convolutional neural network

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Determination of the level of degradation of generator stator bar insulation using a onedimensional convolutional neural network

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2024 International Conference on Diagnostics in Electrical Engineering (Diagnostika) : /conference proceedings/

ISBN

979-8-3503-6149-0

ISSN

2464-7071

e-ISSN

2464-708X

Počet stran výsledku

4

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Pilsen, Czech Republic

Datum konání akce

3. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001345150300010