Assessing wind farm reliability using weather dependent failure rates

G Wilson, D McMillan

Research output: Contribution to journalConference Contribution

11 Citations (Scopus)

Abstract

Using reliability data comprising of two modern, large scale wind farm sites and wind data from two onsite met masts, a model is developed which calculates wind speed dependant failure rates which are used to populate a Markov Chain. Monte Carlo simulation is then exercised to simulate three wind farms which are subjected to controlled wind speed conditions from three separate potential UK sites. The model then calculates and compares wind farm reliability due to corrective maintenance and component failure rates influenced by the wind speed of each of the sites. Results show that the components affected most by changes in average daily wind speed are the control system and the yaw system. A comparison between this model and a more simple estimation of site yield is undertaken. The model takes into account the effects of the wind speed on the cost of operation and maintenance and also includes the impact of longer periods of downtime in the winter months and shorter periods in the summer. By taking these factors into account a more detailed site assessment can be undertaken. There is significant value to this model for operators and manufacturers.
LanguageEnglish
Article number012181
Number of pages11
JournalJournal of Physics: Conference Series
Volume524
Issue number1
DOIs
Publication statusPublished - 10 Jun 2014
EventThe Science of Making Torque from Wind - Technical University of Denmark, Copenhagen, Denmark
Duration: 17 Jun 201420 Jun 2014

Fingerprint

weather
Farms
maintenance
yaw
downtime
Markov chains
Markov processes
winter
summer
costs
Control systems
operators

Keywords

  • wind farm
  • reliability
  • weather dependent
  • failure rates
  • instrumentation and measurement
  • earth sciences

Cite this

@article{0bf63db195ff4260b479fc1bab6f3af1,
title = "Assessing wind farm reliability using weather dependent failure rates",
abstract = "Using reliability data comprising of two modern, large scale wind farm sites and wind data from two onsite met masts, a model is developed which calculates wind speed dependant failure rates which are used to populate a Markov Chain. Monte Carlo simulation is then exercised to simulate three wind farms which are subjected to controlled wind speed conditions from three separate potential UK sites. The model then calculates and compares wind farm reliability due to corrective maintenance and component failure rates influenced by the wind speed of each of the sites. Results show that the components affected most by changes in average daily wind speed are the control system and the yaw system. A comparison between this model and a more simple estimation of site yield is undertaken. The model takes into account the effects of the wind speed on the cost of operation and maintenance and also includes the impact of longer periods of downtime in the winter months and shorter periods in the summer. By taking these factors into account a more detailed site assessment can be undertaken. There is significant value to this model for operators and manufacturers.",
keywords = "wind farm , reliability, weather dependent , failure rates, instrumentation and measurement, earth sciences",
author = "G Wilson and D McMillan",
year = "2014",
month = "6",
day = "10",
doi = "10.1088/1742-6596/524/1/012181",
language = "English",
volume = "524",
journal = "Journal of Physics: Conference Series",
issn = "1742-6588",
number = "1",

}

Assessing wind farm reliability using weather dependent failure rates. / Wilson, G; McMillan, D.

In: Journal of Physics: Conference Series, Vol. 524, No. 1, 012181, 10.06.2014.

Research output: Contribution to journalConference Contribution

TY - JOUR

T1 - Assessing wind farm reliability using weather dependent failure rates

AU - Wilson, G

AU - McMillan, D

PY - 2014/6/10

Y1 - 2014/6/10

N2 - Using reliability data comprising of two modern, large scale wind farm sites and wind data from two onsite met masts, a model is developed which calculates wind speed dependant failure rates which are used to populate a Markov Chain. Monte Carlo simulation is then exercised to simulate three wind farms which are subjected to controlled wind speed conditions from three separate potential UK sites. The model then calculates and compares wind farm reliability due to corrective maintenance and component failure rates influenced by the wind speed of each of the sites. Results show that the components affected most by changes in average daily wind speed are the control system and the yaw system. A comparison between this model and a more simple estimation of site yield is undertaken. The model takes into account the effects of the wind speed on the cost of operation and maintenance and also includes the impact of longer periods of downtime in the winter months and shorter periods in the summer. By taking these factors into account a more detailed site assessment can be undertaken. There is significant value to this model for operators and manufacturers.

AB - Using reliability data comprising of two modern, large scale wind farm sites and wind data from two onsite met masts, a model is developed which calculates wind speed dependant failure rates which are used to populate a Markov Chain. Monte Carlo simulation is then exercised to simulate three wind farms which are subjected to controlled wind speed conditions from three separate potential UK sites. The model then calculates and compares wind farm reliability due to corrective maintenance and component failure rates influenced by the wind speed of each of the sites. Results show that the components affected most by changes in average daily wind speed are the control system and the yaw system. A comparison between this model and a more simple estimation of site yield is undertaken. The model takes into account the effects of the wind speed on the cost of operation and maintenance and also includes the impact of longer periods of downtime in the winter months and shorter periods in the summer. By taking these factors into account a more detailed site assessment can be undertaken. There is significant value to this model for operators and manufacturers.

KW - wind farm

KW - reliability

KW - weather dependent

KW - failure rates

KW - instrumentation and measurement

KW - earth sciences

UR - http://www.scopus.com/inward/record.url?scp=84903689343&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/524/1/012181

DO - 10.1088/1742-6596/524/1/012181

M3 - Conference Contribution

VL - 524

JO - Journal of Physics: Conference Series

T2 - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

IS - 1

M1 - 012181

ER -