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Optimal Replacement Strategies for Wind Energy Systems

Flory, John (2013) Optimal Replacement Strategies for Wind Energy Systems. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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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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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairKharoufeh, Jeffreyjkharouf@pitt.eduJKHAROUF
Committee MemberMaillart, Lisamaillart@pitt.eduMAILLART
Committee MemberNorman, Bryanbanorman@pitt.eduBANORMAN
Committee MemberGebraeel,
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

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