Condition-Based Maintenance for Wind Farms Using a Distributionally Robust Chance Constrained Program

Operations and Maintenance (O&M) expenses account for up to 30% of the operating costs of wind farms. Condition-based maintenance (CBM) strategies, which incorporate predictive analytics into maintenance optimization, have been proven to be effective in reducing O&M costs in wind farms. Existing predictive CBM strategies for wind farms rely on the assumption that predictive analytics can accurately estimate the remaining lifetime distribution (RLD) of wind turbines, allowing for the direct implementation of stochastic programming or threshold-based policies. However, estimated RLDs can be inaccurate due to noisy sensors or limited training data. To address this issue, this paper develops a CBM strategy for wind farms that uses a Distributionally Robust Chance Constrained (DRCC) optimization model. Our formulation acknowledges that estimated distributions may be inaccurate and so seeks solutions that are robust against distribution perturbations within a Wasserstein ambiguity set. We show that the proposed DRCC optimization problem can be exactly reformulated as an integer linear program. We derive methods to strengthen the Big-M values of this reformulation, thereby enabling the DRCC model to be efficiently solved by off-the-shelf optimization software. The proposed strategy is validated through computational studies using real-world and synthetic degradation data, outperforming stochastic programming and robust optimization benchmark models.