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Multi-Objective Real-Time Tuning of SVC Used in Electrified Traction Systems

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50020136" target="_blank" >RIV/62690094:18470/22:50020136 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.mdpi.com/1424-8220/22/4/1584" target="_blank" >https://www.mdpi.com/1424-8220/22/4/1584</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3390/s22041584" target="_blank" >10.3390/s22041584</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Multi-Objective Real-Time Tuning of SVC Used in Electrified Traction Systems

  • Original language description

    Electric train system is a very large load for the power network. This load consumes a large amount of reactive power. In addition, it causes a massive unbalance to the network, which results in many problems such as voltage drops, high transmission losses, reduction in the transformer output ability, negative sequence current, mal-operation of protective relays, etc. In this paper, a novel real-time optimization approach is presented to adjust the static VAR compensator (SVC) for the traction system to realize two objectives; current unbalance reduction and reactive power compensation. A multi-objective optimization technique entitled non-dominated sorting genetic algorithm (NSGA-II) is used to fulfill the regarded objectives simultaneously. A comprehensive simulator has been designed for electric train network modeling that is able to adjust the parameters of SVC in an optimum manner at any time and under any circumstances. The results illustrate that the provided method can efficiently reduce the unbalancing in current as well as supply the demanded reactive power with acceptable precision.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2022

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    SENSORS

  • ISSN

    1424-8220

  • e-ISSN

    1424-8220

  • Volume of the periodical

    22

  • Issue of the periodical within the volume

    4

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    18

  • Pages from-to

    "Article Number: 1584"

  • UT code for WoS article

    000771468100001

  • EID of the result in the Scopus database

    2-s2.0-85125277552