Flory, John
(2013)
Optimal Replacement Strategies for Wind Energy Systems.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
This is the latest version of this item.
Abstract
Motivated by rising energy prices, global climate change, escalating demand for electricity and global energy supply uncertainties, the U.S. government has established an ambitious goal of generating 80% of its electricity supply from clean, renewable sources by 2035. Wind energy is poised to play a prominent role in achieving this goal as it is estimated that 20% of the total domestic electricity supply can be reliably generated by land-based and offshore wind turbines by 2030. However, the cost of producing wind energy remains a significant barrier with operating and maintenance (O&M) costs contributing 20 to 47.5% of the total cost of energy. Given the urgent need for clean, renewable energy sources, and the widespread appeal of wind energy as a viable alternative, it is imperative to develop effective techniques to reduce the O&M costs of wind energy.
This dissertation presents a framework within which real-time, condition-based data can be exploited to optimally time the replacement of critical wind turbine components. First, hybrid analytical-statistical tools are developed to estimate the current health of the component and approximate the expected time at which it will fail by observing a surrogate signal of degradation. The signal is assumed to evolve as a switching diffusion process, and its parameters are estimated via a novel Markov chain Monte Carlo procedure. Next, the problem of optimally replacing a critical component that resides in a partially-observable environment is addressed. Two models are formulated using a partially-observed Markov decision process (POMDP) framework. The first model ignores the cost of turbine downtime, while the second includes this cost explicitly. For both models, it is shown that a threshold replacement policy is optimal with respect to the cumulative level of component degradation. A third model is presented that considers cases in which the environment is partially observed and degradation measurements are uncertain. A threshold policy is shown to be optimal for a special case of this model. Several numerical examples will illustrate the main results and the value of including environmental observations in the wind energy setting.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
25 September 2013 |
| Date Type: |
Publication |
| Defense Date: |
24 June 2013 |
| Approval Date: |
25 September 2013 |
| Submission Date: |
5 July 2013 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
135 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Industrial Engineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Wind turbine
Optimal replacement
Degradation
Switching diffusion model
Partially-observed Markov decision process (POMDP)
Maintenance optimization |
| Additional Information: |
This is a revised final draft of my dissertation. The dissertation has been modified to conform to the ETD formatting guidelines. |
| Date Deposited: |
25 Sep 2013 14:31 |
| Last Modified: |
15 Nov 2016 14:14 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/19278 |
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Optimal Replacement Strategies for Wind Energy Systems. (deposited 25 Sep 2013 14:31)
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