Identification of Linearised RMS-Voltage Dip Patterns Based on Clustering in Renewable Plants

Eduard Muljadi, Tania Garcia-Sanchez, Emilio Gomez-Lazaro, Mathieu Kessler, Irene Munoz-Benavente, Angel Molina-Garcia

Research output: Contribution to journalArticlepeer-review

9 Scopus Citations


Generation units connected to the grid are currently required to meet low-voltage ride-through (LVRT) requirements. In most developed countries, these requirements also apply to renewable sources, mainly wind power plants and photovoltaic installations connected to the grid. This study proposes an alternative characterisation solution to classify and visualise a large number of collected events in light of current limits and requirements. The authors' approach is based on linearised root-mean-square-(RMS)-voltage trajectories, taking into account LRVT requirements, and a clustering process to identify the most likely pattern trajectories. The proposed solution gives extensive information on an event's severity by providing a simple but complete visualisation of the linearised RMS-voltage patterns. In addition, these patterns are compared to current LVRT requirements to determine similarities or discrepancies. A large number of collected events can then be automatically classified and visualised for comparative purposes. Real disturbances collected from renewable sources in Spain are used to assess the proposed solution. Extensive results and discussions are also included in this study.
Original languageAmerican English
Pages (from-to)1256-1262
Number of pages7
JournalIET Generation, Transmission and Distribution
Issue number6
StatePublished - 2018

NREL Publication Number

  • NREL/JA-5D00-68187


  • clustering
  • low-voltage ride-through
  • LVRT
  • voltage dip


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