https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
FEATURE EXTRACTION AND CLASSIFICATION OF TRANSIENT FAULT RECORDS
Mälardalen University, School of Innovation, Design and Engineering.
2023 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

As the power distribution system grows and more sensors are added, more data is created every day. This data can be crucial for finding faults, but there is now so much data that it ends up being unused. This presents a valuable opportunity to gain crucial insights into the continuously expanding and increasingly complex power distribution system. 

This thesis aims to utilize this valuable resource by finding a feature extraction method that can find valuable features in real-world data, use these features to cluster the data, separate different faults into different clusters, and develop a method for how these clusters can be classified, making it possible for an expert to classify large amounts of data quickly. 

In the end, an autoencoder was used for the feature extraction. The features could be used to cluster both labeled and unlabeled real-world data. The clustering also made it possible to find errors in the labeled data, as the data from one class were clustered into two clusters. A method was developed that allowed the clusters of 32454 unlabeled datapoints to be accurately classified in approximately 30 minutes. 

This thesis has successfully developed a method that can be used to get insights from large amounts of data, helping experts within the field of power engineering build the power distribution system of the future.

Place, publisher, year, edition, pages
2023. , p. 77
National Category
Robotics
Identifiers
URN: urn:nbn:se:mdh:diva-63409OAI: oai:DiVA.org:mdh-63409DiVA, id: diva2:1770730
External cooperation
Hitachi Energy Research
Supervisors
Examiners
Available from: 2023-06-27 Created: 2023-06-19 Last updated: 2023-06-27Bibliographically approved

Open Access in DiVA

fulltext(5135 kB)190 downloads
File information
File name FULLTEXT01.pdfFile size 5135 kBChecksum SHA-512
a0769b35f32cf7f424ca4ba2a66b53c97a67867d135d96dd40369a3ec45d238a51780d766080ddb4d7ab8e61909635215d8319d6a8d0638ac8afda8adefb80bb
Type fulltextMimetype application/pdf

By organisation
School of Innovation, Design and Engineering
Robotics

Search outside of DiVA

GoogleGoogle Scholar
Total: 190 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 292 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf