Posters

Abstract

Operation and maintenance (O&M) cost accounts for a substantial part of a wind farm's cost in its lifetime [1]. Due to its cost advantages, opportunistic maintenance strategies have been widely studied for wind turbine applications. Traditionally, wind farm operators use a preventive replacement maintenance strategy, where certain components are replaced at scheduled intervals. An opportunistic maintenance strategy views the time when a preventive replacement or a stochastic failure happens as an opportunity to carry out certain maintenance works. In such a strategy, many decisions need to be taken, which can be optimized to reduce the cost of maintenance, maintain the reliability and minimize the lost power production. Recently, an opportunistic maintenance strategy has been proposed by Wang et al. [2] to reduce the O&M cost of wind turbines. This strategy includes two sets of reliability thresholds to handle subassemblies in operational and failed wind turbines and has ten different maintenance modes for subassemblies in different conditions. Imperfect maintenance is described by Weibull distribution with an age reduction factor. The effectiveness of this strategy is validated by comparing to a traditional preventive replacement strategy with Monte Carlo simulation. In this study, the opportunistic maintenance strategy is implemented in Python and further optimized using the Random Search algorithm to reduce the expected maintenance cost, while constraints on the system reliability level are considered. A remark 17.8% reduction of the expected total maintenance cost is achieved for a 1.5MW operating in 20 years, showing the effectiveness of the proposed methodology.