The development of floating offshore wind farms (FOWF) represents a significant advancement in the renewable energy sector, offering a scalable solution to harness wind energy in deep-water locations. This thesis investigates the effectiveness of multiline anchor systems in improving the cost-efficiency, operation, and maintainability of FOWFs. Utilizing the modified Windfarm Operation and Cost-benefit Analysis Tool (WOMBAT), this research comprehensively analyzes multiple case studies, focusing on critical factors such as cost, profit, operational performance, and maintenance occurrences.

Case Study 3 compares the performance of multiline anchor systems against traditional single-line configurations in the Morro Bay scenario. The results indicate that multiline systems significantly enhance availability, reduce operational expenditure, and improve overall profit. Case Study 4 extends this analysis to the Gulf of Maine, confirming that multiline systems maintain their advantages under varied environmental conditions and extended operational lifespans. This study underscores the robustness and adaptability of multiline anchors in diverse offshore environments.

Case Study 5 provides a sensitivity analysis to evaluate the reliability of the simulation inputs. It demonstrates that the fundamental benefits of multiline systems remain consistent despite variations in mooring line failure rates. However, it also underscored the importance and need for available data and how the model and its outputs are only as credible as the inputted values.

The findings contribute to the offshore wind industry by providing actionable insights into the deployment of multiline mooring systems and offering recommendations for enhancing the cost-effectiveness and reliability of FOWFs. The research also highlights opportunities for further development of the WOMBAT tool, particularly in incorporating proactive maintenance strategies and environmental factors such as wind and wave directions.

This thesis supports the broader adoption of multiline anchor systems in floating offshore wind farms. With a demonstrated lower cost in operation and maintenance, it advocates for their strategic deployment in various environmental conditions to drive the transition to a sustainable energy future.

One of the world’s biggest concerns is global warming, a solution to this can be wind energy. Offshore wind energy has advantages over onshore wind energy, however, the levelized cost of energy is higher. The maintenance costs are a major cost contributor. To lower these costs, research is performed on faults and its detection. Currently, little is known about fault detectability and vibration propagation in a drive train of an offshore wind turbine. Fault detection and vibration propagation in a drive train of a 10 MW floating offshore wind turbine is therefore investigated to get an insight about the effect of faults on the vibration monitoring data of a drive train. Three different faults with five different degradation levels are applied one by one on the bearings of a 10 MW drive train model. These faults are radial and axial damage in the main shaft front bearing and radial damage in the high speed shaft rear bearing. One traditional, two non-traditional and two novel fault detection methods are used to detect faults and their vibration propagation. One common and one novel fault detection method are deployed in the time domain: the Velocity Root-Mean-Square (RMS) Threshold Method and the Peeters’ Anomaly Detection Method. The Velocity RMS Threshold Method compares the RMS of the vibration velocity of non-rotating parts with a threshold proposed by ISO 10816-21. The latter method makes use of statistical indicators and is tailored for this study. Although changes after fault introduction were observable, the methods can not be used and need to be altered for usage in the wind industry. The non-traditional Angular Velocity Error Energy Method is deployed in the frequency domain. It makes use of the angular velocity measurements from the drive train’s shafts and compares the normalized energy of its spectra with a threshold. This method inspired the development of novel fault detection methods introduced in this study, being the Bearing Velocity Energy Method (making use of bearing velocity measurements and also based on the Velocity Root-Mean-Square Threshold Method) and the Shaft Vibration Energy Method (making use of the velocity and acceleration of shafts). Both methods compare the normalized energy of the spectra with a threshold. Radial damage in the main shaft front bearing could be detected using the Angular Velocity Error Energy Method, the Bearing Velocity Energy Method and the Shaft Vibration Energy Method. Damage was detectable from 15% degradation onwards. Next to a change in vibration in the main shaft and its bearings, a different vibration behaviour was observed at the planet carrier front and rear bearing, intermediate speed shaft front bearing and on the low speed shaft. Axial damage in the main shaft front bearing could only be detected using the Shaft Vibration Energy Method. It was shown that this kind of damage was detectable by monitoring the main shaft’s vibration from 50% degradation and higher. Radial damage in the high speed shaft rear bearing could be detected using the Bearing Velocity Error Method and the Shaft Vibration Energy Method. Damage could only be detected for degradation higher than 70%, by monitoring the high speed shaft and its bearings. Next to the typical measurement locations, it is recommended to place extra sensors measuring velocity on the first stage planet carrier front and rear bearing housings, intermediate speed shaft front bearing housings and on the low speed shaft. The outcome of this study contributes to the understanding of vibration propagation and fault detection in a drive train. The fault detection methods can be implemented in maintenance and monitoring methods for offshore wind turbines. Maintenance engineers can use the detected vibration propagation to check the affected gearbox components and replace them before they fail.

Power optimization through wake steering and axial induction control is a well investigated topic in wind energy, which is generally proven to work. The influence of control manoeuvres on the fatigue of static components is generally discussed, but drivetrain fatigue due to wake steering and axial induction control is rarely discussed, while it is known that the drivetrain is a highly vulnerable part of the wind turbine and its downtime can result in a significant increase in cost. Having a better understanding of turbine wake interaction and wind farm power optimization and its influence on drivetrain dynamic behaviour serves as a reference for future wind farm cost optimization and predictive maintenance. The main research question answered in the thesis is as follows: To what extent does wind farm power optimization increase profit when wind farm power production and drivetrain bearing fatigue damage is considered? Multiple test cases for wake steering and axial induction control are considered, where different yaw angles, γ, and the blade pitch angles, β, are chosen for the upwind turbine. For each test case, power production and bearing damage is studied. A cost estimation is made and for a range of energy prices, the most profitable test case is found. For verification, a two and four wind turbine case in an uniform wind field is considered. Power production results for this low turbulent case are studied and compared to literature. Turbulent wind field results show that both wake steering and induction control result in a limited power production increase of 0.78% for γ = 7° and 0.17% for β = 1°, shown in the Figure below. The power production increase for the two and four wind turbine case in the uniform wind field for wake steering and induction control are 4.78% for γ = 15°, 16.6% for γ = 20°, 0.19% for β = 1° and 5.04% for β = 3° respectively. Overall absolute bearing damage of WT1 and WT2 increases with increased yaw angles for WT1 and the overall bearing damage of WT1 and WT2 decreases with increased blade pitch angles for WT1. INP-A and PLC-B bearing damage significantly increased for the downwind turbine. In the high turbulent wind field (TI = 0.2), when considering two wind turbines, wake steering can result in an increase of profit ranging from -€3,70 to €4,-, while axial induction control can result in an increase in profit ranging from €3,- to €40,-. In the low turbulent wind field (TI = 0), when considering four wind turbines, the power production increase for wake steering can result in a profit increase ranging from €30,- to €130,-, while axial induction control can result in a profit increase ranging from €15,- to €60,-. Both wake steering and induction control can result in increased profit. The desired control manoeuvre is highly dependent on the ambient wind, wake overlap of the downwind turbine and the wind farm arrangement.

Premature failures in large offshore Wind Turbines are often attributed to bearing failure despite gearboxes being designed and developed using the best bearing design practices. Furthermore, as turbine size and rated power increase, bearings display an enhanced tendency to fail. Unscheduled bearing replacement at sea is a complex, costly, weather-dependent and time-consuming operation that results in high turbine downtimes. Market trends show an increase in turbine rated capacity and a noticeable shift towards deeper waters and far-off remote sites which further delays and complicates unscheduled maintenance activities and aggravates the cost penalties of idle turbines. Detecting an incipient bearing fault (diagnosis task) is therefore a major aspect to evaluate drivetrain and overall wind turbine reliability. Moreover, estimating the remaining useful life of bearings and predicting their operational state in the future (prognosis task) can achieve a breakthrough in optimising maintenance programs, improve wind farm operation and decrease wind turbine downtime which can bring about a significant cost reduction. The purpose of this work is to investigate the health monitoring and prognostics possibilities of drivetrain bearings in a floating spar-buoy offshore wind turbine. The drivetrain concept considered in this work is based on DTU’s 10-MW reference wind turbine. Specifically, this study targets the prognosis of four critical drivetrain bearings located in the main shaft and the high-speed shaft.

The offshore wind industry has grown rapidly over the last decade and drivetrains are increasing in size to reduce the cost of energy. These turbines are operating in a harsh environment. Adopting a preventive maintenance strategy is important to achieve an as high as possible availability of the farm and reduce the cost of maintenance. A well performing condition monitoring system that utilizes SCADA data from the wind farm can enable this strategy without the need in additional cost in hardware. This master thesis focusses on the development of a framework that can be utilized for this task. This framework can process raw operational SCADA data collected at the Egmond aan Zee offshore wind farm to create a clean dataset to train supervised machine learning models on. This work provides an insight in the correlation between different SCADA signals using a mathematical approach and from a understanding of the system integration of drivetrain components. Bearing temperatures are modelled using a data driven approach to describe the temperatures under healthy conditions. Several models are evaluated for this task and it was concluded that a decision tree supervised machine learning regression model resulted in the lowest error between predicted and measured values. Anomalies are detected and tracked with a normal behaviour model and a Sherward and CUSUM control chart that are applied on the residual error between modelled and measured temperature signals. 4 anomalies could be identified in the gearbox bearings using the developed framework. Abnormal behaviour of the drivetrain could be identified as early as 1 month before the turbine was taken out of productions. This highlights that temperature based condition monitoring that utilizes SCADA data can be used for early detection of faults by combining the accuracy of supervised machine learning methods with different fault detection methods like the CUSUM control chart. This work also investigates the relation between experienced wake of a wind turbine and the influence on the drivetrain component temperatures. The wake conditions at Egmond aan Zee, modelled with an Ishahara wake model, and the component temperature measurements from the SCADA data are used for this analysis. The bearing temperature distributions under different operational and wake conditions can be compared by clustering over the wind speed and the velocity deficit or turbulence intensity at turbine level. It is concluded from this work that wake effects do not result in a change in drivetrain component temperatures. The effects of asymmetric wake conditions opposed to wake experienced over the entire rotor is analysed by comparing the temperature distributions under these conditions in a cluster where the turbine is partially waked. A small shift towards higher component temperatures can observed on a limited amount of data for turbines under asymmetric loading conditions.