Operation and Maintenance (O&M) costs are estimated to account for 14%-30% of total Offshore Wind Farm (OWF) project lifecycle expenditure according to a range of studies. In this respect, identifying factors affecting operational costs and availability are vital for wind farm operators to achieve the most profitable decisions. Many OWFs are built in stages and the important factors may not be consistent for the different phases. To address this issue, three OWF case studies are defined to represent two phases and a complete project. An initial qualitative screening sensitivity analysis was conducted to identify the most important factors of O&M affecting operating cost and availability. The study concluded that the important factors for total O&M cost were access and repair costs along with failure rates for both minor and major repairs. For time-based availability, the important factors identified were those related to the length of time conducting the maintenance tasks, i.e. the operation duration and the working day length. It was found that the two stages had similar results, but these were different compared to the complete project. In this case, the results provide valuable information to OWF operators during the project development and decision making process
This article explores the application of a wind farm layout evaluation function and layout optimization framework to Middelgrunden wind farm in Denmark. This work applies an evaluation tool to estimate the cost, energy production, and the levelized cost of energy for the existing layout at Middelgrunden wind farm; comparing these against the cost and energy production reported by the wind farm. From here, new layouts have then been designed using both genetic algorithms and particle swarm optimization. This study has found that both algorithms are capable of identifying layouts with reduced levelized cost of energy compared to the existing layout while still considering the specific conditions and constraints experienced by this site. Reductions in levelized cost of energy, such as this can result in very significant savings over the lifetime of the project thereby highlighting the importance of including new advanced methods to wind farm layout design
This article explores the application of a binary genetic algorithm and a binary particle swarm optimizer to the optimization of an offshore wind farm layout. The framework developed as part of this work makes use of a modular design to include a detailed assessment of a wind farm’s layout including validated analytic wake modeling, cost assessment, and the design of the necessary electrical infrastructure considering constraints. This study has found that both algorithms are capable of optimizing wind farm layouts with respect to levelized cost of energy when using a detailed, complex evaluation function. Both are also capable of identifying layouts with lower levelized costs of energy than similar studies that have been published in the past and are therefore both applicable to this problem. The performance of both algorithms has highlighted that both should be further tuned and benchmarked in order to better characterize their performance.
Failure Modes Effect Analysis (FMEA), or more specifically, Failure Modes Effect and Criticality Analysis (FMECA) has been accepted as an effective condition monitoring assessment tool used widely by the mili-tary, traditional industries and reliability relevant engineering systems. A successful FMEA assists to identity, evaluate and report component failure modes, their severity and impact on the systems. FMEA has been al-ready applied to onshore wind turbines, but there is a lack of offshore wind turbine applications. FMEA can be quantified by using the metric of Risk Priority Number (RPN), defined as the product of the levels of event severity, occurrence frequency and detectability. This paper presents an approach that allows the application of RPN to offshore wind energy by identifying correction factors to existing onshore RPN values taken from previous research. This approach estimates offshore failure rates for key wind turbine components from onshore data
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.