• Energy Research

Abstract Justifying continued development and large-scale deployment of Wave Energy Converters (WECs) requires quantification of the potential resource. Currently, estimates are available for individual countries or, at low accuracy, for global resource. Additionally, existing estimates do not provide insight into potential future markets, i.e. the location of the resource. Here, NOAA WaveWatch III data are analysed for a 6-year period to calculate wave energy potential. The global market is then quantified by calculating the energy flux across a line 30 nautical miles offshore. Results are presented by country, continent, hemisphere and for the globe. Confidence values are also presented in the form of 95% confidence intervals. These limits provide insight into the uncertainty associated with the length of dataset used and the variability of the resource. This enables direct comparison with other resource assessment studies, whether using numerical model or measured data. An extensive survey of previous global and regional resource estimates is also conducted, in order to compare both results and methods. Supplementing this, extractable resource is estimated by considering the deployment of an illustrative WEC (Pelamis P2). The global wave power resource is 2.11 ± 0.05 TW, of which 4.6% is extractable with the chosen WEC configuration.

Presentation in session 7.3 on the H2020 ReaLCoE project: WP4: Design of 10MW+ turbine for optimised operations & maintenance

Presentation from session 8.1 (AI for wind) Application of Machine Learning (Gaussian Processes) to the reconstruction of wind fields from scanning Lidar data.

AbstractThis paper describes the SPOWTT project. The intention of this project was to understand how sailing by crew transfer vessel (CTVs) to offshore wind farms affects the mental and physical wellbeing of individuals on board. The focus was on quantifying this impact, understanding the key drivers, with an aim to ensuring personnel can arrive to the wind turbines in a fit state to work safely and effectively. Impacts looked at subjective state beyond simply vomiting. Key results include the ability now to predict vessel motions from given Metocean conditions and vessel designs. We also discovered that the impact of vessel motions on seasickness is different for different symptoms and is driven not only by vertical z‐axis accelerations but also by certain frequencies of motion in the y‐axis. Frequencies other than 0.16 Hz were found to be impactful, and x‐axis movements appeared to have a longer‐lasting effect on the day's work. Through the formulation of a new, evidence‐based understanding of seasickness, we have created an operational planning tool, designed to have a direct benefit on the safety and productivity of offshore wind farm operations.

Offshore wind farm managers and schedulers need to manage large numbers of wind turbine visits every day, in order to: repair minor faults; conduct inspections; and perform scheduled service operations. Daily schedules form a choice of which maintenance activities to conduct, taking account of: constraints on weather conditions, shifts, vessel and technician capabilities and availability; and the impact of activities on wind farm profitability. This forms a formidable optimisation challenge that today is solved “by hand” by a scheduler. The work presented here contains three aspects of importance. First, a powerful and flexible metaheuristic optimisation model is developed to solve this problem, where the simulation algorithms and objective can be altered without any change to the optimiser. Second, a practical valuation methodology is developed, where historic wind farm data can be used to identify strengths and weaknesses in any maintenance planning method and estimate financial return on investment from implementation. Finally, the methodology described is implemented and tested, by applying the valuation methodology to data from the Princess Amalia Wind Park in The Netherlands. Even given the limited scope of this case study, automating daily maintenance planning can bring significant financial benefits: 302 kV over 5 months

The paper provides a brief overview of the aims and main results of IEA Wind Task 33. IEA Wind Task 33 was an expert working group with a focus on data collection and reliability assessment for O & M optimization of wind turbines. The working group started in 2012 and finalized the work in 2016. The complete results of IEA Wind Task 33 are described in the expert group report on recommended practices for "Wind farm data collection and reliability assessment for O & M optimization" which will be published by IEA Wind in 2017. This paper briefly presents the background of the work, the recommended process to identify necessary data, and appropriate taxonomies structuring and harmonizing the collected entries. Finally, the paper summarizes the key findings and recommendations from the IEA Wind Task 33 work.