Abstract
The wind energy industry is expanding in order to be able to meet the current and future energy demand, and is supported by governments in that renewable energy investment has been made. Optimal decision making (DM) in wind turbine manufacturing is required to guarantee the competitiveness of the business. This paper considers decision making for wind turbine manufacturing using a logical decision tree (LDT) and binary decision diagrams (BDD). A qualitative analysis of wind turbine manufacturing is carried out using logical decision trees. They are used for a qualitative study of the case study. Binary decision diagrams are used to obtain the Boolean function and, therefore, to carry out a quantitative analysis. Finally, an optimization of budgets is employed based on importance measures. There is no optimal method that can establish the importance measures. The following heuristic methods have been used to find a solution close to the optimal: Fussell-Vesely, Birnbaum and Criticality. The computational cost is reduced by ranking the events. The heuristic methods to establish the best rankings are: Top-Down-Left-Right, Level based method, AND based method, Breadth-First Search (BFS) and Depth First Search (DFS). A real case study is considered, in which a static and dynamic analysis is carried out.
Highlights
Wind energy has been growing in recent years
Vachon [3] shows that operations and maintenance (OM) costs can make up 75–90% of the investment costs, based on a 20-year life cycle for a 100-MW wind farm with 600 turbines of 750 kW
MP, that is obtained by the conversion of study is done considering the Boolean expression to set the MP, that is obtained by the conversion of the logical decision tree (LDT) to the binary decision diagrams (BDD)
Summary
Wind energy has been growing in recent years. Vachon [3] shows that operations and maintenance (OM) costs can make up 75–90% of the investment costs, based on a 20-year life cycle for a 100-MW wind farm with 600 turbines of 750 kW each. The cost per failure is increasing, larger turbines may reduce the OM cost per unit power [4]. The correct decision making (DM) in design, manufacture and performance of wind turbines must be set correctly. This will mean that operational and maintenance costs and downtimes can be minimized or avoided [5,6].
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