Antenna contactless partial discharges detection in covered conductors using ensemble stacking neural networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10250517" target="_blank" >RIV/61989100:27240/23:10250517 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27730/23:10250517 RIV/61989100:27740/23:10250517

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0957417422019285" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0957417422019285</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2022.118910" target="_blank" >10.1016/j.eswa.2022.118910</a>

Result language

angličtina

Original language name

Antenna contactless partial discharges detection in covered conductors using ensemble stacking neural networks

Original language description

High impedance faults caused by vegetation are difficult to detect when covered conductors in medium voltage overhead power lines are used. Long-term contact of XLPE insulation with vegetation causes partial discharges (PDs) which damage the insulation. Although a cheap and easy to install, contactless detection method was developed using an antenna, there is a lack of classification algorithms for this method. Only two custom machine learning algorithms have been tested so far, and both rendered unsatisfactory results for the real application. This work investigates the use of neural network algorithms for this problem and the application of heterogeneous stacking ensembles using neural networks. We used real data collected from a number of detection stations in the Czech Republic. Also, we limited ourselves to supporting edge computing using devices such as Edge TPU. We propose the application of a heterogeneous stacking ensemble neural network to classify PDs obtained by the contactless method. The algorithm we propose is based on a stacking ensemble with a novel combination of base learners, and the Wide and Deep neural network is used as a meta-learner. We compared the results of our algorithm with other algorithms designated for time series classification. Also, an ablation study of the ensemble was conducted, and satisfactory results were obtained using the proposed algorithm. The ensemble outperformed all algorithms tested and is usable on the edge using AI HW accelerator as the ensemble is only feedforward and contains only well-used and known layers. This research improves our understanding of the classification of PDs using the contactless PD detection method and also introduces a stacking ensemble of convolutional neural network and autoencoders for a time series classification for the first time. (C) 2022 The Authors

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF19_073%2F0016945" target="_blank" >EF19_073/0016945: Doctoral grant competition VŠB - TU Ostrava</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

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

Name of the periodical

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

1873-6793

Volume of the periodical

213

Issue of the periodical within the volume

A

Country of publishing house

GB - UNITED KINGDOM

Number of pages

15

Pages from-to

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UT code for WoS article

000874659200002

EID of the result in the Scopus database

2-s2.0-85139592339