Abstract
A two-stage optimization model has been developed for an offshore wind farm that integrates layout design and turbine maintenance policy-making. In Stage 1, first, the optimal development of the offshore wind resource aims to maximize the wind energy production by seeking the optimal turbine layout under uncertainty of wind conditions, in which the optimal number of turbines N and their productive placement are determined. Then, the locations of N turbines are further optimized for maximal energy production. Due to the unique maintenance challenges for offshore wind farm, in Stage 2, we develop computational tools for a novel opportunistic condition-based maintenance policy, in which the periodic inspection intervals are chosen to ensure the reliable energy production with limited maintenance costs. In this study, probabilistic models are built for stochastic wind speeds and directions. We apply Monte Carlo simulation for sampling wind data from the wind probabilistic models considering multiple seasonal scenarios. The algorithm efficiency of the two-stage optimization framework is demonstrated based on the results of a case of wind farm development along the New Jersey coast.
| Original language | English |
|---|---|
| Pages (from-to) | 288-297 |
| Number of pages | 10 |
| Journal | Computers and Industrial Engineering |
| Volume | 120 |
| DOIs | |
| Publication status | Published - Jun 1 2018 |
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Keywords
- Condition-based maintenance
- Offshore wind farm
- Opportunistic maintenance
ASJC Scopus subject areas
- Computer Science(all)
- Engineering(all)
Cite this
Integrated optimization of offshore wind farm layout design and turbine opportunistic condition-based maintenance. / Song, Sanling; Li, Qing; Felder, Frank; Wang, Honggang; Coit, David W.
In: Computers and Industrial Engineering, Vol. 120, 01.06.2018, p. 288-297.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Integrated optimization of offshore wind farm layout design and turbine opportunistic condition-based maintenance
AU - Song, Sanling
AU - Li, Qing
AU - Felder, Frank
AU - Wang, Honggang
AU - Coit, David W.
PY - 2018/6/1
Y1 - 2018/6/1
N2 - A two-stage optimization model has been developed for an offshore wind farm that integrates layout design and turbine maintenance policy-making. In Stage 1, first, the optimal development of the offshore wind resource aims to maximize the wind energy production by seeking the optimal turbine layout under uncertainty of wind conditions, in which the optimal number of turbines N and their productive placement are determined. Then, the locations of N turbines are further optimized for maximal energy production. Due to the unique maintenance challenges for offshore wind farm, in Stage 2, we develop computational tools for a novel opportunistic condition-based maintenance policy, in which the periodic inspection intervals are chosen to ensure the reliable energy production with limited maintenance costs. In this study, probabilistic models are built for stochastic wind speeds and directions. We apply Monte Carlo simulation for sampling wind data from the wind probabilistic models considering multiple seasonal scenarios. The algorithm efficiency of the two-stage optimization framework is demonstrated based on the results of a case of wind farm development along the New Jersey coast.
AB - A two-stage optimization model has been developed for an offshore wind farm that integrates layout design and turbine maintenance policy-making. In Stage 1, first, the optimal development of the offshore wind resource aims to maximize the wind energy production by seeking the optimal turbine layout under uncertainty of wind conditions, in which the optimal number of turbines N and their productive placement are determined. Then, the locations of N turbines are further optimized for maximal energy production. Due to the unique maintenance challenges for offshore wind farm, in Stage 2, we develop computational tools for a novel opportunistic condition-based maintenance policy, in which the periodic inspection intervals are chosen to ensure the reliable energy production with limited maintenance costs. In this study, probabilistic models are built for stochastic wind speeds and directions. We apply Monte Carlo simulation for sampling wind data from the wind probabilistic models considering multiple seasonal scenarios. The algorithm efficiency of the two-stage optimization framework is demonstrated based on the results of a case of wind farm development along the New Jersey coast.
KW - Condition-based maintenance
KW - Offshore wind farm
KW - Opportunistic maintenance
UR - http://www.scopus.com/inward/record.url?scp=85046631755&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046631755&partnerID=8YFLogxK
U2 - 10.1016/j.cie.2018.04.051
DO - 10.1016/j.cie.2018.04.051
M3 - Article
AN - SCOPUS:85046631755
VL - 120
SP - 288
EP - 297
JO - Computers and Industrial Engineering
JF - Computers and Industrial Engineering
SN - 0360-8352
ER -