Employing an optimal maintenance policy for an offshore wind power system is very important to decrease budgets or costs while increasing reliability. Moreover, a maintenance threshold is useful at determining such a policy when obtaining the lowest possible maintenance cost. Given the limited works on the topic, this paper proposes a two-step approach to determine the optimal maintenance threshold for multiple components of an offshore wind power system to minimize maintenance cost while achieving the highest possible system reliability. Factors such as weather conditions, system reliability, power generation loss, and electricity market price are critical to the system’s cost model. First, using a genetic algorithm, a dynamic strategy is developed to determine the maintenance threshold of an individual component in which failure rate and maintenance cost are vital parameters of the cost model. Second, fuzzy multi-objective programming is utilized to determine the optimal maintenance threshold of a whole offshore wind power system where all components are considered. When maintenance threshold results are compared, component-wise versus system-wise, the study obtains average system savings of 1.62% on maintenance cost, while system reliability rises by an average of 1.21%.

Employing an optimal maintenance policy for an offshore wind power system is very important to decrease budgets or costs while increasing reliability. Moreover, a maintenance threshold is useful at determining such a policy when obtaining the lowest possible maintenance cost. Given the limited works on the topic, this paper proposes a two-step approach to determine the optimal maintenance threshold for multiple components of an offshore wind power system to minimize maintenance cost while achieving the highest possible system reliability. Factors such as weather conditions, system reliability, power generation loss, and electricity market price are critical to the system’s cost model. First, using a genetic algorithm, a dynamic strategy is developed to determine the maintenance threshold of an individual component in which failure rate and maintenance cost are vital parameters of the cost model. Second, fuzzy multi-objective programming is utilized to determine the optimal maintenance threshold of a whole offshore wind power system where all components are considered. When maintenance threshold results are compared, component-wise versus system-wise, the study obtains average system savings of 1.62% on maintenance cost, while system reliability rises by an average of 1.21%.

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