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Evaluation of a Local Fault Detection Algorithm for HVDC Systems

  • M.J. Perez Molina
  • , P. Eguia Lopez
  • , M. Larruskain Eskobal
  • , M. Santos Mugica
  • , R. Rodriguez Sanchez

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

2 Citas (Scopus)

Resumen

A great increase in the amount of energy generated from clean and renewable sources integrated in the electric power system is expected worldwide in the coming years. High Voltage Direct Current (HVDC) systems are seen as a promising alternative to the traditional Alternating Current (AC) systems for the expansion of the electric power system. However, to achieve this vision, there are some remaining challenges regarding HVDC systems which need to be solved. One of the main challenges is related to fault detection and location in HVDC grids. This paper reviews the main protection algorithms available and presents the evaluation of a local fault detection algorithm for DC faults in a multi-terminal Voltage Source Conversion (VSC) based HVDC grid. The paper analyses the influence of the DC voltage sampling frequency and the cable length in the performance of the algorithm. © 2019, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ).
Idioma originalInglés
Páginas (desde-hasta)262-267
Número de páginas6
PublicaciónRenewable Energy and Power Quality Journal
Volumen17
DOI
EstadoPublicada - jul 2019

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

  1. ODS 7: Energía asequible y no contaminante
    ODS 7: Energía asequible y no contaminante

Palabras clave

  • DC faults
  • Fault detection
  • Local protection system
  • Multiterminal
  • VSC-HVDC grid
  • Protection algorithm

Project and Funding Information

  • Funding Info
  • The authors thank the support from the Spanish Ministry of Economy, Industry and Competitiveness (project ENE2016-79145-R AEI/FEDER, UE) and GISEL research group IT1083-16), as well as from the University of the Basque Country UPV/EHU (research group funding PPG17/23).

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