Date of Award
8-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Industrial Engineering
Committee Chair/Advisor
Jeffrey P. Kharoufeh
Committee Member
Amin Khademi
Committee Member
Yongjia Song
Committee Member
Brian Fralix
Abstract
This dissertation is concerned with devising optimal replacement policies for offshore wind turbines with a focus on minimizing the costs associated with major component replacements and production losses due to downtime. Like their onshore counterparts, offshore wind turbines are subject to progressive degradation due to normal operations, as well as the influence of dynamic environmental conditions that influence their rate of degradation. Due to their proximity, wind farm turbines share common environmental conditions, as well as specialized maintenance resources. Their common exposure to the environment and need to share resources introduce both stochastic and economic dependence between the wind turbines. The primary objective of this research is to optimally prescribe the timing of major component replacements in offshore wind farms so that long-run expected costs are minimized.
First, we examine structured optimal replacement policies subject to these dependencies using a Markov decision process (MDP) framework. We find that a degradation-based threshold policy exists and is optimal. Structural properties of the cost function and optimal replacement policies are proved analytically, and these results offer practical guidance for operators seeking to balance cost-effectiveness while accounting for both stochastic and economic dependencies. While the MDP framework is helpful, it may be difficult (or impossible) to obtain optimal replacement policies for realistically-sized wind farms due to the curses of dimensionality. To address this computational challenge, near-optimal policies are obtained by devising an approximate linear programming (ALP) formulation that can be efficiently solved using a column generation algorithm. This approach facilitates computation of value function bounds and achieves near-optimal solutions exhibiting an optimality gap of approximately 1%. We also establish sufficient conditions under which the optimal policy can be retrieved from the approximate policy and establish a performance bound to evaluate its accuracy. Numerically, we explore how setup costs, the number of environment states, and the number of turbines in the wind farm influence replacement policies. Finally, the MDP framework is extended to consider the problem of jointly optimizing replacement and jack-up vessel (JUV) requesting decisions in a wind farm containing both a fixed-base and a floating offshore wind (FOW) turbine. Environment states in shallow and deep waters are modeled as a (weakly) positively correlated bivariate Markov chain. We formulate an infinite-horizon MDP model that captures stochastic dependence from the correlated degradation processes and economic dependence from shared JUV usage. We derive structural properties of the optimal policy and show that both replacement and JUV requesting decisions exhibit a threshold-type structure in the degradation levels. Additionally, we establish sufficient conditions for the policy to be monotone in the environment states. These results provide practical insights for coordinated maintenance planning across heterogeneous turbine types in offshore settings.
Recommended Citation
Soltani, Morteza, "Replacement Optimization for Offshore Wind Turbine Farms" (2025). All Dissertations. 3981.
https://open.clemson.edu/all_dissertations/3981