Logistic and service optimization for O&M of offshore wind farms; model development & output analysis
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Abstract
The offshore wind industry is growing fast with an average annual market growth rate of almost 7% in the next five years. Service of wind turbines has proven to be expensive and difficult, especially offshore. A well-coordinated support organisation, optimized logistic and maintenance strategies are required to effectively reduce the costs associated with wind farm support. The thesis work focuses on developing a stochastic time based Logistic and Service model to analyse wind farm maintenance and logistic support organisation. The primary objective is to obtain the most cost-effective strategy, besides improving the availability of the wind farm. Within the scope of project, the wind turbine technology and the causes of its failures are reviewed. From the sub-assembly components, a list of critical spare parts is short-listed. As part of the data analysis and field studies, reliability patterns of the wind turbines are obtained from the Fraunhofer IWES WMEP database. The spare part support organisation is based on two running wind farms, namely Nysted and OWEZ. The weather information provided by the FINO 1 and FINO 2 MET masts are employed to estimate the accessibility to the wind farm. Further, based on the classification of the maintenance type of spare parts, the logistic and service strategies are implemented. The optimization of logistic and service model has been done separately, where the best possible values of ordering parameters and service aspects like finding the right access vessel strategy, the crew strength, the shift patterns, the feasibility of an offshore accommodation or renting of huge ships like mother vessel are explored. To verify the working of the model, sensitivity analysis, comparison studies and extreme value testing are performed. The model is able to predict a suitable strategy for a given wind farm, which is shown by implementing the model for a planned wind farm. With the developed O&M model, accurate inventory stocks, downtime, availability, service and logistic parameters and hence the inventory and maintenance cost is obtained. The results from the reference farms are encouraging as different strategies are compared for a cost-effective solution.