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Structural damage in offshore wind jacket support structures are relatively unlikely due to the precautions taken in design but it could imply dramatic consequences if undetected. This work explores the possibilities of damage detection when using low resolution data, which are available with lower costs compared to dedicated high-resolution structural health monitoring. Machine learning approaches showed to be generally feasible for detecting a structural damage based on SCADA data collected in a simulation environment. Focus is here given to investigate model uncertainties, to assess the applicability of machine learning approaches for reality. Two jacket models are utilised representing the as-designed and the as-installed system, respectively. Extensive semi-coupled simulations representing different operating load cases are conducted to generate a database of low-resolution signals serving the machine learning training and testing. The analysis shows the challenges of classification approaches, i.e. supervised learning aiming to separate healthy and damage status, in coping with the uncertainty in system dynamics. Contrarily, an unsupervised novelty detection approach shows promising results when trained with data from both, the as-designed and the as-installed system. The findings highlight the importance of investigating model uncertainties and careful selection of training data.

In the estimation of future investments in the offshore wind industry, the operation and maintenance (O&M) phase plays an important role. In the simulation of the O&M figures, the weather conditions should contain information about the waves’ main characteristics and the wind speed. As these parameters are correlated, they were simulated by using a multivariate approach, and thus by generating vectors of measurements. Four different stochastic weather time series generators were investigated: Markov chains (MC) of first and second order, vector autoregressive (VAR) models, and long short-term memory (LSTM) neural networks. The models were trained on a 40-year data set with 1 h resolution. Thereafter, the models simulated 25-year time series, which were analysed based on several time series metrics and criteria. The MC (especially the one of second order) and the VAR model were shown to be the ones capturing the characteristics of the original time series the best. The novelty of this paper lies in the application of LSTM models and multivariate higher-order MCs to generate offshore weather time series, and to compare their simulations to the ones of VAR models. Final recommendations for improving these models are provided as conclusion of this paper.

Despite several potential advantages, relatively few studies and design support tools have been developed for floating vertical axis wind turbines. Due to the substantial aerodynamics differences, the analyses of vertical axis wind turbine on floating structures cannot be easily extended from what have been already done for horizontal axis wind turbines. Therefore, the main aim of the present work is to compare the dynamic response of the floating offshore wind turbine system adopting two different mooring dynamics approaches. Two versions of the in-house aero-hydro-mooring coupled model of dynamics for floating vertical axis wind turbine (FloVAWT) have been used, employing a mooring quasi-static model, which solves the equations using an energetic approach, and a modified version of floating vertical axis wind turbine, which instead couples with the lumped mass mooring line model MoorDyn. The results, in terms of mooring line tension, fatigue and response in frequency have been obtained and analysed, based on a 5 MW Darrieus type rotor supported by the OC4-DeepCwind semisubmersible.

To date, the focus of the research on offshore wind turbines (WTs) has been mainly on how to minimise their capital cost, but Operation and Maintenance (O&M) can represent up to a third of the lifetime costs of an offshore wind farm. The cost for the assets repair/replacement and for the logistics of the maintenance operations are two of the biggest contributors to O&M expenses. While the first is going to rise with the employment of bigger structures, the latter can significantly increase dependently on the reliability of the components, and thus the necessity to performed unscheduled maintenance operations. Using the reliability data for a population of offshore WTs (representing the configurations most employed offshore), first, the share of the components failures to the O&M cost, together with an estimation of their dependency on some O&M parameters has been derived. Then, by following a cost-based Failure Modes Effects and Criticality Analysis (FMECA), and ranking the components through O&M cost priority number, the most critical components for O&M unplanned operations are identified.

With single components weighing up to hundreds of tonnes and lifted to heights of approximately 100 m, offshore wind turbines can pose risks to personnel, assets, and the environment during installation and maintenance interventions. Guidelines and standards for health and safety in lifting operations exist; however, having people directly beneath the load is still common practice in offshore wind turbine installations. Concepts for human-free offshore lifting operations in the categories of guidance and control, connections, and assembly are studied in this work. This paper documents the process of applying Multi-Criteria Decision Analysis (MCDA), using experts’ opinions for the importance of defined criteria obtained by conducting an industry survey, to benchmark the suitability of the concepts at two stages. Stage one streamlined possible options and stage two ranked the remaining suite of options after further development. The survey results showed that criteria such as ‘reduction of risk’, ‘handling improvement’ and ‘reliability of operation’ were most important. The most viable options, weighted by industry opinion, to remove personnel from areas of high risk are: Boom Lock and tag lines, a camera system with mechanical guidance, and automated bolt installation/fastening for seafastening. The decision analysis framework developed can be applied to similar problems to inform choices subject to multiple criteria.

The interest in floating offshore wind turbines (FOWT) has been growing substantially over the last decade and, after a number of prototypes deployed [1], the first offshore floating wind farms have been approved and are being developed. While a number of international research activities have been conducted on the dynamics of offshore floating HAWT systems (e.g. OC3-Phase IV2, OC4-Phase II3), relatively few studies have been conducted on floating VAWT systems, despite their potential advantages [2]. Due to the substantial differences between HAWT and VAWT aerodynamics, the analyses on floating HAWT cannot be extended to floating VAWT systems. The main aim of the present work is to compare the dynamic response of the FOWT system adopting two different mooring dynamics approaches. Two version of the in-house aero-hydro-mooring coupled model of dynamics for VAWT “FloVAWT” [3] are used: one which adopts a mooring quasi-static model, and solves the equations using an energetic approach [4], and a modified version of FloVAWT, which uses instead the lumped-mass mooring line model “MoorDyn” [5]. The floating VAWT system considered is based on a 5MW Darrieus type rotor supported by the OC4-Phase II3 semi-submersible. The results for the considered metocean conditions show that MoorDyn approach estimate larger translational displacements of the platform, compared to the quasi-static rigid approach previously implemented in FloVAWT. As expected, the magnitudes of the forces along the lines are lower, being part of the energy employed for the elastic deformation of the cables. A systematic comparison of the differences between the two approaches is presented.

With single elements weighing up to hundreds of tonnes and lifted to heights of 100 meters, offshore wind turbines can pose risks to personnel, assets, and the environment during installation and maintenance interventions. To increase safety during offshore lifts, this study focuses on solutions for human-free lifting operations. Ideas in the categories of logistics, connections, as well as guidance and control, were discussed and ranked by means of a multi-criteria decision analysis. Based upon 38 survey responses weighting 21 predefined decision criteria, the most promising concepts were selected. Logistically, pre-assembled systems would reduce the number of lifts and thus reduce the risk. A MATLAB-based code has been developed to optimise installation time, lifted weight, and number of lifts. Automated bolting and seafastening solutions have high potential to increase safety during the transport of the wind turbine elements and, additionally, speed up the process. Finally, the wind turbine should be lifted on top of the support structure without having personnel being under the load. A multi-directional mechanical guiding element has been designed and tested successfully in combination with visual guidance by cameras in a small-scale experiment.

This paper gives a comparative analysis of the performance ofa wind turbine direct drive train system with PMSG and fullyrated converters when two different wind turbine models areused for its assessment. The impact of the advanced AeroHydro-Servo-Elastic model, and the most common analyticalfunction wind turbine model, on the generator-converterdynamics are assessed. Results corresponding to the systemresponse to steps up/down in wind speed and turbulent windspeed conditions are presented and discussed in the paper. Itis found that below rated speed there is no significantdifference between results obtained from both models. Aboverated wind speed there exist some differences among results.

The best practise for structural damage detection currently relies on the installation of structural health monitoring systems for the collection of dedicated high frequency measurements. Switching to the employment of the wind turbine’s SCADA (Supervisory Control and Data Acquisition) signals and their commonly recorded low frequency statistics can lead to a reduction in the number of ad-hoc monitoring sensors and quantity of data required. In this paper, aero-hydro-servo-elastic simulations for a model of a turbine are used to assess its loads and any changes in the dynamics under healthy state and a damaged configuration case study. To prove the feasibility of the damage detection through low-resolution data, the statistics of the typically recorded signals from the SCADA and the structural monitoring systems are fed into a database for training and testing of classification algorithms. The ability of the machine learning models to generalise the classification for both stochasticity and uncertainties in the environmental conditions are tested. Decision tree-based classifiers showed the capability to capture the damage for the majority of the operating conditions considered. Though the setup of the traditional SCADA sensors had to be supplemented with an additional structural health monitoring sensor, the detection of the damage has been shown feasible by referring to low-frequency statistics only.