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
Data-driven assessment of VI diagrams for inference on pantograph quantities waveform distortion in AC railways
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0002-3625-8578
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0001-5845-5620
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0002-4004-0352
Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Genoa, 16145, Italy.
2024 (English)In: Computers & electrical engineering, ISSN 0045-7906, E-ISSN 1879-0755, Vol. 120, no Part B, article id 109730Article in journal (Refereed) Published
Abstract [en]

This work proposes an application of unsupervised deep learning (DL) on 2-D images containing VI diagrams of measured railway pantograph quantities to find patterns in operating conditions (OCs) and waveform distortion. Measurement data consist of pantograph voltage and current measurements from a Swiss 15 kV 16.7 Hz commercial locomotive and a French 2x25 kV 50 Hz test-dedicated locomotive, containing more than 4000 records of 5-cycle snippets for each system. The variational autoencoder (VAE), followed by feature clustering, finds patterns in the input data. Each cluster captures patterns from the VI diagrams, which contain information from current and voltage waveshapes and sub-second variations. The time-domain admittance allows inference about the rolling stock (RS) operation and the waveform distortion spectra, including harmonics and supraharmonics characteristics from both RS and traction supply. The VAE successfully performs data embedding using only 16 channels in the latent space. The effectiveness of the method is quantified by means of the mean square reconstruction error (never larger than 1.5% and equal to 0.31% and 0.33% on average for the Swiss and French case, respectively). The t-SNE visualization confirms that overlapping of clusters is negligible, with a percentage of “misplaced” cluster points of 2.18% and 2.50%, again for the Swiss and French case, respectively. The computation time for the VAE prediction could be brought to some tens of ms representing a performance reference for future implementations. The proposed VI diagram assessment covers emissions for different OCs, rapid changes in power supply conditions, and background distortion caused by other trains on the same line, including line and impedance changes due to the moving load. In this perspective physical justification is found by domain knowledge integration for the identified clusters. A concluding discussion regarding advantages, limitations, and potential improvements or diversification is also included.

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 120, no Part B, article id 109730
Keywords [en]
Dimension reduction, Pattern analysis, Power quality, Power system harmonics, Load monitoring, Guideway transportation
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-110282DOI: 10.1016/j.compeleceng.2024.109730ISI: 001368585500001Scopus ID: 2-s2.0-85205289816OAI: oai:DiVA.org:ltu-110282DiVA, id: diva2:1904076
Note

Validerad;2024;Nivå 2;2024-11-11 (joosat);

Full text license: CC BY 4.0;

Funder: Swedish Transport Administration; 

Available from: 2024-10-08 Created: 2024-10-08 Last updated: 2025-03-11Bibliographically approved
In thesis
1. Assessment of Waveform Distortion Interactions in Electric Railway Power Systems
Open this publication in new window or tab >>Assessment of Waveform Distortion Interactions in Electric Railway Power Systems
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Railway electrified systems are one of the most popular and essential forms of transportation globally, and the performance of those systems impacts society. The electric railway power systems (ERPS) comprehend the infrastructure and apparatus that aims to deliver power for the rolling stocks in different types of railway transportation. Due to the broad application of static power electronics, ERPS is characterized by several sources of waveform distortion. Waveform distortion is a critical power quality (PQ) issue and a challenge to managing electromagnetic compatibility (EMC) in railway systems. It englobes harmonics (disturbances synchronous with the fundamental power frequency up to 2 kHz), interharmonics (disturbances asynchronous with the fundamental power frequency up to 2 kHz), and supraharmonics (synchronous and asynchronous disturbances between 2 and 150 kHz).

The ERPS has several system complexities that should be taken into consideration when assessing waveform distortion related to the characteristics of the phenomena: extensive distribution system with intricate circuit arrangements and moving single-phase loads; multiple voltage levels and electromagnetic environments, including railway grid and subsystems, as well as public grid; waveform distortion has time-varying behavior dependent on operating states of rolling stock, traffic plan, grid balancing, and spatial position of the vehicles; a mix between traditional equipment or infrastructure and population of new power electronic conversion stages with a lack of guidelines and standardization; and variety of waveform distortion sources and signatures.

The objective of this research is to gain knowledge and a better understanding of waveform distortion, including not only harmonics but also interharmonics and supraharmonics in railways systems, to characterize emission sources, propagation, the impact of the operation on time-varying behaviors in several scales, interaction among systems and subsystems, and adverse effects. The focus of the work is alternating current (AC) electrified railways, with a deeper assessment of, but not limited to, the railway system solution of Sweden (15 kV 16 ⅔ Hz). The development and scope of this work provide a comprehensive literature review of waveform distortion assessment for electrical railway power systems and build up a framework for future contributions, characterization of waveform distortion for electrical railway power systems using measurements, conduct detailed measurements on waveform distortion in a traction converter station, modeling waveform distortion propagation for ERPS considering complexities of the system, application of unsupervised deep learning (DL) methods to find patterns in waveform distortion data and investigation of the impacts related with those issues. The research contributions from those defined scopes are summarized below.

·         Identification of the challenges of waveform distortion assessment in ERPS and categorizing the available literature to address some of those challenges.

·         Characterization and screening of the waveform distortion time-varying dependencies in different time scales.

·         Providing a methodology for assessing time-varying waveform distortion in railway systems, adapting traditional methodologies, advanced statistical analysis, and machine learning approaches.

·         Modeling waveform distortion interaction within the ERPS in Sweden, incorporating challenges such as moving loads, meshed grid analyses, and a wide range of disturbances propagation in ERPS.

·         Addressing the different mechanisms affecting waveform distortion at the catenary and public grid sides.

·         Investigation of the impact of waveform distortion performance on associated equipment.

The work provides crucial steps for better establishing a PQ framework and future standardization for waveform distortion in ERPS by exploring multiple aspects and directions on the assessment side.

Place, publisher, year, edition, pages
Luleå University of Technology, 2025
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords
waveform distortion, harmonics, interharmonics, supraharmonics, power quality, harmonic analysis railway power system, guideway transportation, electrified railways
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Power Systems and Components
Research subject
Electric Power Engineering
Identifiers
urn:nbn:se:ltu:diva-111979 (URN)978-91-8048-787-0 (ISBN)978-91-8048-788-7 (ISBN)
Public defence
2025-05-07, Hörsal A, Luleå University of Technology, Skellefteå, 09:00 (English)
Opponent
Supervisors
Funder
Swedish Transport Administration, 24579
Available from: 2025-03-11 Created: 2025-03-11 Last updated: 2025-04-11Bibliographically approved

Open Access in DiVA

fulltext(4345 kB)24 downloads
File information
File name FULLTEXT02.pdfFile size 4345 kBChecksum SHA-512
49fa3173a897c78499a4bb01550b8c47ce95ee5a2b57b2bb84ac48606a689284360212887cf0d71bca77c7dfc70765b3e6edfdeae652a8250023f2c8098ce9a8
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Salles, Rafael S.de Oliveira, Roger A.Rönnberg, Sarah K.

Search in DiVA

By author/editor
Salles, Rafael S.de Oliveira, Roger A.Rönnberg, Sarah K.
By organisation
Energy Science
In the same journal
Computers & electrical engineering
Other Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 33 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

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 208 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