Abstract
Offshore wind is considered a crucial part in the future energy supply. However, influenced by weather conditions, the maintenance of offshore wind turbine system (OWTs) equipment is challenged by poor accessibility and serious failure consequences. It is necessary to study the optimized strategy of comprehensive maintenance for offshore wind farms, with consideration of the influences of incomplete equipment maintenance, weather accessibility and economic relevance. In this paper, a Monte Carlo algorithm-improved factor is presented to simulate the imperfect preventive maintenance activity, and waiting windows were created to study the accessibility of weather conditions. Based on a rolling horizon approach, an opportunity group maintenance model of an offshore wind farm was proposed. The maintenance correlations between systems and between equipment as well as breakdown losses, maintenance uncertainty, and weather conditions were taken into account in the model, thus realizing coordination of maintenance activities of different systems and different equipment. The proposed model was applied to calculate the maintenance cost of the Dafengtian Offshore Wind Farm in China. Results proved that the proposed model could realize long-term dynamic optimization of offshore wind farm maintenance activities, increase the total availability of the wind power system and reduce total maintenance costs.
Highlights
Marine engineering equipment is usually located far away from the coastline
Once the maintenance engineering team arrives at the offshore wind farm, attention should be paid to multiple wind turbine systems contained in the wind farm, and each offshore wind system contains multiple equipment under simultaneous operation
In order to solve the problem of maintenance economic relevance among various equipment and the availability of each wind turbine in the whole offshore wind farm, it can be considered to evaluate the working status of other wind turbines and other equipment when carrying out maintenance work on a certain equipment for a certain wind turbine, and repair other wind turbine equipment close to the reliability threshold in advance, so as to reduce the maintenance frequency of the whole offshore wind farm and reduce its overall maintenance cost
Summary
Marine engineering equipment is usually located far away from the coastline. The complicated and varied marine environment increases the failure rate of components, and brings great inconvenience to maintenance work. Hagen [15] proposed a multivariate Markov chain model for state estimation of sea conditions based on observation time sequences, weather conditions were expressed by wave height, wind velocity and wind direction, and maintenance strategies of the offshore wind farm were optimized This model considered the influence of weather accessibility on the maintenance work, but neglected economic relevance between systems and equipment, and overall maintenance cost of the wind farm was not obviously reduced. Taking optimal economical efficiency as the objective function, this model considers economic relevance between equipment and equipment, system and system during the maintenance process, which improves accuracy of the decision-making model of total expected offshore wind farm maintenance cost; taking Dafengtian wind farm of Goldwind Technology Co., Ltd. as an example, Section 5 verifies the proposed maintenance optimization model, and results show that considering weather accessibility accords with actual offshore wind farm maintenance work, Energies 2021, 14, 965 but compared with independent repair of wind turbines or equipment, the opportunistic grouping maintenance scheme can effectively reduce the cost, the reasonability and applicability of this model are verified
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