Sensitivity of offshore wind turbine operation & maintenance costs to key operational parameters

Iain Allan Dinwoodie, David McMillan, Francis Quail

Research output: Contribution to conferencePaper

Abstract

Due to the lack of operator knowledge and the deployment of new technology in future large offshore wind farms, significant uncertainty exists in the field of offshore wind farm operation and maintenance (O&M). In order to investigate this uncertainty as well as explore the feasibility of novel O&M strategies, simulation is required. This paper outlines a new modelling approach which allows identification of the key costs and operational parameters of O&M in order to quantify the sensitivity of overall O&M costs to variations of these parameters. In addition, several key areas requiring greater understanding are identified and two case studies demonstrating how the developed model can provide new insights into these areas are presented.

This paper applies an auto-regressive (AR) climate modelling approach to concurrently simulate representative wind and wave time series coupled with Markov Chain Monte Carlo (MCMC) based failure simulation. Both these simulation approaches are well established in the field of reliability modelling however, this work represents the first time that both wind and wave climate simulations have been coupled and applied to offshore wind O&M.
The AR climate modelling approach allows a synthetic time series to be rapidly produced based on site data. The hourly short term correlations as well as medium term access windows of up to several days required for maintenance operations are captured while preserving the overall observed distribution. The seasonality observed in wind speed and wave heights is also incorporated.

Failures are simulated based on failure rates available in the public domain and associated time to repair is based on the simulated weather climate. This time series based approach allows constraints on access vehicle capabilities, type and availability to be applied and the influence on wind farm availability and O&M costs examined. Lost earnings associated with downtime are also captured using the simulated wind speed time series.

An initial study, examining the influence of reliability and time to repair of key components on overall availability and costs is presented for different sized turbines demonstrating how the benefits of reduced maintenance action is affected by turbine size. A further study, exploring the degree to which overall O&M costs are influenced by variation in vessel hire costs is also shown, demonstrating the capability of the modelling approach. Finally, various potential novel areas for investigation are identified for future work highlighting the benefit of the modelling approach.

Conference

ConferenceEuropean Safety Reliability and Data Analysis Conference ESReDA 2012
CountryUnited Kingdom
CityGlasgow
Period15/05/1216/09/12

Fingerprint

wind turbine
wind farm
cost
time series
turbine
repair
modeling
simulation
climate modeling
wind velocity
wave climate
wind wave
Markov chain
wave height
parameter
seasonality
vessel
weather
climate

Keywords

  • sensitivity
  • maintenance costs
  • wind turbine operation
  • offshore
  • operational parameters

Cite this

Dinwoodie, I. A., McMillan, D., & Quail, F. (2012). Sensitivity of offshore wind turbine operation & maintenance costs to key operational parameters. Paper presented at European Safety Reliability and Data Analysis Conference ESReDA 2012, Glasgow, United Kingdom.
Dinwoodie, Iain Allan ; McMillan, David ; Quail, Francis. / Sensitivity of offshore wind turbine operation & maintenance costs to key operational parameters. Paper presented at European Safety Reliability and Data Analysis Conference ESReDA 2012, Glasgow, United Kingdom.8 p.
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title = "Sensitivity of offshore wind turbine operation & maintenance costs to key operational parameters",
abstract = "Due to the lack of operator knowledge and the deployment of new technology in future large offshore wind farms, significant uncertainty exists in the field of offshore wind farm operation and maintenance (O&M). In order to investigate this uncertainty as well as explore the feasibility of novel O&M strategies, simulation is required. This paper outlines a new modelling approach which allows identification of the key costs and operational parameters of O&M in order to quantify the sensitivity of overall O&M costs to variations of these parameters. In addition, several key areas requiring greater understanding are identified and two case studies demonstrating how the developed model can provide new insights into these areas are presented. This paper applies an auto-regressive (AR) climate modelling approach to concurrently simulate representative wind and wave time series coupled with Markov Chain Monte Carlo (MCMC) based failure simulation. Both these simulation approaches are well established in the field of reliability modelling however, this work represents the first time that both wind and wave climate simulations have been coupled and applied to offshore wind O&M. The AR climate modelling approach allows a synthetic time series to be rapidly produced based on site data. The hourly short term correlations as well as medium term access windows of up to several days required for maintenance operations are captured while preserving the overall observed distribution. The seasonality observed in wind speed and wave heights is also incorporated.Failures are simulated based on failure rates available in the public domain and associated time to repair is based on the simulated weather climate. This time series based approach allows constraints on access vehicle capabilities, type and availability to be applied and the influence on wind farm availability and O&M costs examined. Lost earnings associated with downtime are also captured using the simulated wind speed time series. An initial study, examining the influence of reliability and time to repair of key components on overall availability and costs is presented for different sized turbines demonstrating how the benefits of reduced maintenance action is affected by turbine size. A further study, exploring the degree to which overall O&M costs are influenced by variation in vessel hire costs is also shown, demonstrating the capability of the modelling approach. Finally, various potential novel areas for investigation are identified for future work highlighting the benefit of the modelling approach.",
keywords = "sensitivity , maintenance costs , wind turbine operation, offshore , operational parameters",
author = "Dinwoodie, {Iain Allan} and David McMillan and Francis Quail",
year = "2012",
language = "English",
note = "European Safety Reliability and Data Analysis Conference ESReDA 2012 ; Conference date: 15-05-2012 Through 16-09-2012",

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Dinwoodie, IA, McMillan, D & Quail, F 2012, 'Sensitivity of offshore wind turbine operation & maintenance costs to key operational parameters' Paper presented at European Safety Reliability and Data Analysis Conference ESReDA 2012, Glasgow, United Kingdom, 15/05/12 - 16/09/12, .

Sensitivity of offshore wind turbine operation & maintenance costs to key operational parameters. / Dinwoodie, Iain Allan; McMillan, David; Quail, Francis.

2012. Paper presented at European Safety Reliability and Data Analysis Conference ESReDA 2012, Glasgow, United Kingdom.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Sensitivity of offshore wind turbine operation & maintenance costs to key operational parameters

AU - Dinwoodie, Iain Allan

AU - McMillan, David

AU - Quail, Francis

PY - 2012

Y1 - 2012

N2 - Due to the lack of operator knowledge and the deployment of new technology in future large offshore wind farms, significant uncertainty exists in the field of offshore wind farm operation and maintenance (O&M). In order to investigate this uncertainty as well as explore the feasibility of novel O&M strategies, simulation is required. This paper outlines a new modelling approach which allows identification of the key costs and operational parameters of O&M in order to quantify the sensitivity of overall O&M costs to variations of these parameters. In addition, several key areas requiring greater understanding are identified and two case studies demonstrating how the developed model can provide new insights into these areas are presented. This paper applies an auto-regressive (AR) climate modelling approach to concurrently simulate representative wind and wave time series coupled with Markov Chain Monte Carlo (MCMC) based failure simulation. Both these simulation approaches are well established in the field of reliability modelling however, this work represents the first time that both wind and wave climate simulations have been coupled and applied to offshore wind O&M. The AR climate modelling approach allows a synthetic time series to be rapidly produced based on site data. The hourly short term correlations as well as medium term access windows of up to several days required for maintenance operations are captured while preserving the overall observed distribution. The seasonality observed in wind speed and wave heights is also incorporated.Failures are simulated based on failure rates available in the public domain and associated time to repair is based on the simulated weather climate. This time series based approach allows constraints on access vehicle capabilities, type and availability to be applied and the influence on wind farm availability and O&M costs examined. Lost earnings associated with downtime are also captured using the simulated wind speed time series. An initial study, examining the influence of reliability and time to repair of key components on overall availability and costs is presented for different sized turbines demonstrating how the benefits of reduced maintenance action is affected by turbine size. A further study, exploring the degree to which overall O&M costs are influenced by variation in vessel hire costs is also shown, demonstrating the capability of the modelling approach. Finally, various potential novel areas for investigation are identified for future work highlighting the benefit of the modelling approach.

AB - Due to the lack of operator knowledge and the deployment of new technology in future large offshore wind farms, significant uncertainty exists in the field of offshore wind farm operation and maintenance (O&M). In order to investigate this uncertainty as well as explore the feasibility of novel O&M strategies, simulation is required. This paper outlines a new modelling approach which allows identification of the key costs and operational parameters of O&M in order to quantify the sensitivity of overall O&M costs to variations of these parameters. In addition, several key areas requiring greater understanding are identified and two case studies demonstrating how the developed model can provide new insights into these areas are presented. This paper applies an auto-regressive (AR) climate modelling approach to concurrently simulate representative wind and wave time series coupled with Markov Chain Monte Carlo (MCMC) based failure simulation. Both these simulation approaches are well established in the field of reliability modelling however, this work represents the first time that both wind and wave climate simulations have been coupled and applied to offshore wind O&M. The AR climate modelling approach allows a synthetic time series to be rapidly produced based on site data. The hourly short term correlations as well as medium term access windows of up to several days required for maintenance operations are captured while preserving the overall observed distribution. The seasonality observed in wind speed and wave heights is also incorporated.Failures are simulated based on failure rates available in the public domain and associated time to repair is based on the simulated weather climate. This time series based approach allows constraints on access vehicle capabilities, type and availability to be applied and the influence on wind farm availability and O&M costs examined. Lost earnings associated with downtime are also captured using the simulated wind speed time series. An initial study, examining the influence of reliability and time to repair of key components on overall availability and costs is presented for different sized turbines demonstrating how the benefits of reduced maintenance action is affected by turbine size. A further study, exploring the degree to which overall O&M costs are influenced by variation in vessel hire costs is also shown, demonstrating the capability of the modelling approach. Finally, various potential novel areas for investigation are identified for future work highlighting the benefit of the modelling approach.

KW - sensitivity

KW - maintenance costs

KW - wind turbine operation

KW - offshore

KW - operational parameters

UR - http://www.esreda.org/

M3 - Paper

ER -

Dinwoodie IA, McMillan D, Quail F. Sensitivity of offshore wind turbine operation & maintenance costs to key operational parameters. 2012. Paper presented at European Safety Reliability and Data Analysis Conference ESReDA 2012, Glasgow, United Kingdom.