A reliability-and-cost-based fuzzy approach to optimize preventive maintenance scheduling for offshore wind farms. (1st June 2019)

• Record Type: Journal Article
• Authors: Zhong, Shuya; Pantelous, Athanasios A.; Goh, Mark; Zhou, Jian
• Is Part Of: Mechanical systems and signal processing. Volume 124(2019)
• Page Start: 643
• Page End: 663
• Publication Date: 2019-06-01
• Subjects: Offshore wind energy -- Preventive maintenance scheduling -- Fuzzy chance-constrained programming -- Fuzzy multi-objective programming -- Reliability -- Maintenance cost; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2019.02.012
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Preventive maintenance of offshore wind farms is discussed in the fuzzy environment. A fuzzy multi-objective non-linear chance-constrained programming model is proposed. Reliability and cost goals, and constraints are newly defined for offshore scenario. A 2-phase solution framework combining operational law and NSGA-II is developed. Corrective maintenance cost is considered to get optimal solution and cost benefit. Abstract: We study the preventive maintenance scheduling problem of wind farms in the offshore wind energy sector which operates under uncertainty due to the state of the ocean and market demand. We formulate a fuzzy multi-objective non-linear chance-constrained programming model with newly-defined reliability and cost criteria and constraints to obtain satisfying schedules for wind turbine maintenance. To solve the optimization model, a 2-phase solution framework integrating the operational law for fuzzy arithmetic and the non-dominated sorting genetic algorithm II for multi-objective programming is developed. Pareto-optimal solutions of the schedules are obtained to form the trade-offs between the reliability maximization and cost minimization objectives. A numerical example is illustrated to validate the model.