Identifier

etd-04112014-091556

Degree

Master of Science in Industrial Engineering (MSIE)

Department

Mechanical Engineering

Document Type

Thesis

Abstract

Although it is a sustainable source and there is abundant potential for energy, cost of energy generated from offshore wind is still high compared to other sustainable energy sources. Apart from the manufacturing cost of turbines, cost of energy is significantly affected by costs of transportation and installation operations of wind turbines and maintenance operations of turbine components. Through optimum selection of decision variables, such as turbine installation method and rated power output of each turbine, cost of transportation and installation operations can be minimized. The first model in this study investigates the impact of these decision variables and effect of learning on cost of transportation and installation and identifies optimal combination of these variables that minimize the total cost. Once the offshore wind farm becomes operational, maintenance cost of the turbines becomes the most significant contributor to the cost of energy. The second model developed in this study put forward a maintenance cost model following multilevel opportunistic preventive maintenance strategy. In this strategy, opportunity for performing preventive actions on components is taken while a failed component is replaced. Total cost associated with maintenance operations depends on the setting of age groups that determine which component should be preventively maintained and to what level of maintenance. Through optimum selection of the number of age groups, cost of maintenance can be minimized. The methodologies for finding optimal solutions for both models are provided, numerical study is performed and sensitivity analyses are presented to illustrate the benefits of the models.

Date

2014

Document Availability at the Time of Submission

Release the entire work immediately for access worldwide.

Committee Chair

Sarker, Bhaba

DOI

10.31390/gradschool_theses.3368

Share

COinS