Identification of linearised RMS-voltage dip patterns based on clustering in renewable plants

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 a...

Full description

Saved in:
Bibliographic Details
Published in:IET generation, transmission & distribution transmission & distribution, 2018-03, Vol.12 (6), p.1256-1262
Main Authors: García-Sánchez, Tania, Gómez-Lázaro, Emilio, Muljadi, Edward, Kessler, Mathieu, Muñoz-Benavente, Irene, Molina-García, Angel
Format: Article
Language:English
Subjects:
clustering
clustering process
generation unit
linearised RMS‐voltage dip pattern identification
linearised RMS‐voltage pattern visualisation
linearised root‐mean‐square‐voltage trajectories
low-voltage ride-through
low‐voltage ride‐through requirements
LVRT
LVRT requirements
pattern classification
pattern clustering
photovoltaic installations
photovoltaic power systems
power generation faults
power grid
power grids
POWER TRANSMISSION AND DISTRIBUTION
real disturbance collection
renewable plant
renewable sources
Research Article
RMS‐voltage trajectories
Spain
voltage dip
wind power plants
Citations: Items that this one cites
Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1751-8687
1751-8695
1751-8695
DOI:10.1049/iet-gtd.2017.0474