O&M cost-based FMECA: Identification and ranking of the most critical components for 2-4 MW geared offshore wind turbines

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

On mooring line tension and fatigue prediction for offshore vertical axis wind turbines: A comparison of lumped mass and quasi-static approaches

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

A methodology to develop reduced-order models to support the operation and maintenance of offshore wind turbines

From an operation & maintenance (O&M) point of view, it is necessary to model the aero-hydro-servo-elastic (AHSE) dynamics of each wind turbine but, on the other side, wind farms generally include hundreds of wind turbines. Simply using and linking several advanced, single wind turbine models of dynamics to represent a wind farm can be computationally prohibitive. To this end, this paper developed a reduced-order model (ROM), able to capture the relevant dynamics of the system for a specific failure, having a lower computational cost and therefore more easily scalable up to a wind farm level. First, a nonlinear AHSE model is used to derive the time-domain response of the wind turbine degrees of freedom (DOFs). The failure mode, its relevant DOF, and the relevant operational conditions during which the failure is likely to occur are identified. A linearisation of the nonlinear aero-hydro-servo-elastic-drivetrain (AHSE-DT) model is then carried out. Subsequently, a number of linear ROMs are developed based on the linear full-order system but excluding high-frequency states using the modal truncation (MT) method. For the targeted DOF (rotor torque signal) and the load cases simulated, the results from the linear ROMs showed that the blade modes are important to capture not only the DOF of extreme values, but also the DOF of high-frequency responses (above 1.5 Hz). The results from nonlinear ROMs showed that the ROM eliminating all the tower modes (rigid tower) is acceptable to capture the DOF of low-frequency response (below 0.5 Hz), as it has almost the same spectral responses as the full-order nonlinear model

Deep neural network hard parameter multi-task learning for condition monitoring of an offshore wind turbine

Abstract: Breaking the curse of small datasets in machine learning is but one of the major challenges that cause several real-life prediction problems. In offshore wind application, for instance, this issue presents when monitoring an asset in an attempt to reduce its infant mortality failures. Another challenge could emerge when reducing the number of sensors installed in order to limit the investment in monitoring systems. To tackle these issues, the aim of this article is to investigate the impact of small data-set on conventional machine learning methods, and to outline the improvement achievable by the implementation of transfer learning approach. It provides a solution to mitigate this issue by applying a hard parameter multi-task learning approach to the supervisory control and data acquisition data from an operational wind turbine, allowing for smaller datasets to efficiently predict the status of the gearbox's vibration data. Two experiments are carried out in this paper. The first is to envisage the possibility of using hard parameter transfer on the operational data from two wind turbines. The second is to compare the results of this model to the findings from a conventional deep neural network model trained on the data from a single turbine

Multi-criteria decision analysis for benchmarking human-free lifting solutions in the offshorewind energy environment

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

Human-free offshore lifting solutions

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

A damage detection and location scheme for offshore wind turbine jacket structures based on global modal properties

Abstract Structural failures of offshore wind substructures might be less likely than failures of other equipments of the offshore wind turbines, but they pose a high risk due to the possibility of catastrophic consequences. Significant costs are linked to offshore operations, like inspections and maintenance activities, thus remote monitoring shows promise for a cost-efficient structural integrity management. This work aims to investigate the feasibility of a two-level detection, in terms of anomaly identification and location, in the jacket support structure of an offshore wind turbine. A monitoring scheme is suggested by basing the detection on a database of simulated modal properties of the structure for different failure scenarios. The detection model identifies the correct anomaly based on three types of modal indicators, namely, natural frequency, the modal assurance criterion between mode shapes, and the modal flexibility variation. The supervised Fisher's linear discriminant analysis is applied to transform the modal indicators to maximize the separability of several scenarios. A fuzzy clustering algorithm is then trained to predict the membership of new data to each of the scenarios in the database. In a case study, extreme scour phenomena and jacket members' integrity loss are simulated, together with variations of the structural dynamics for environmental and operating conditions. Cross-validation is used to select the best hyperparameters, and the effectiveness of the clustering is validated with slight variations of the environmental conditions. The results prove that it is feasible to detect and locate the simulated scenarios via the global monitoring of an offshore wind jacket structure

Failure diagnosis for offshore wind turbines with low availability of run-to-failure data

Despite the efforts to achieve a through-life reliable design and the attempts to control the failures of wind turbines, some system failures are inevitable. The inherent requirement for cost, material, and weight optimisation, together with the extreme operating conditions, can lead to unexpected failures. This is true for land-based turbines and has an even greater impact on offshore wind systems, where the harsh environment and the high cost of the assets and logistics increase the importance of a proactive approach to the system’s maintenance. The smart management of an asset starts with the identification of the health status of its systems, to take cost effective decision on how and when maintain it. The first level of the detection of an anomality in the system comprises the recognition only of the failed status of the asset (level I). Following, the location of the failure should be identified (level II), followed by the detection of its degree of severity (level III) and consequences (level IV). Depending on the availability of continuous monitoring data, historical databases, and advanced numerical models, different frameworks can be established for the failure diagnostics and prognostics. This thesis investigates on the use data-driven, model-based, and digital twin solutions to support the diagnosis of failure events of offshore wind turbine systems characterised by a low availability of run-to-failure data. This topic is of major concern for either the current installations - for which the collection of data is restrained either to only few assets or to more cost-effective temporary monitoring campaign – and the new offshore wind technologies (e.g., floating wind, large-MW structures), for which no or only a limited amount of operating data has been gathered. The mechanical failure of the components of the offshore wind speed conversion system can have a significant impact to the operational expenditure and can be associated to a significant loss of production of the offshore wind farm. The detection of their incipiency has been extensively investigated by machine and deep learning techniques on big sets of condition monitoring and operational data. By contrast, this research explores the implementation of transfer learning to detect anomalies in an offshore wind gearbox with low availability of representative failure data. To move towards the quantification of the consequences of such a failure (level IV), a case of study is used to explore then most suitable the model-reduction techniques to be applied to a full aero-servo-elastic model of the offshore wind turbine. Such a numerical model is the basis for the development of digital twin technology; it is aimed at capturing the only the essential dynamics while targeting the degree(s) of freedom indicating the presence of the failure mode. The presence of a damage in the offshore wind foundation is not commonly recorded, yet structural failures can either lead to catastrophic consequence or considerably increase the cost of maintenance for the planning of expensive subsea inspections. In particular, the fatigue-driven offshore wind jacket foundation designs are sensitive to extreme site conditions, and their expected lifetime can decrease considerably if exposed for a long time to undetected phenomena such as scour and corrosion. This research demonstrates the feasibility of a vibration-based diagnosis (level II) of several damage scenarios for a jacket substructure of an offshore wind turbine. Considering than only a percentage of the assets in the farm are likely to be instrumented with a high-frequency structural health monitoring system, the feasibility of the detection (level I) of a structural failure mode via low-resolution operational data is additionally explored. These virtual monitoring frameworks are supported by the deployment of the digital twin technologies for their setup and their future field application.Despite the efforts to achieve a through-life reliable design and the attempts to control the failures of wind turbines, some system failures are inevitable. The inherent requirement for cost, material, and weight optimisation, together with the extreme operating conditions, can lead to unexpected failures. This is true for land-based turbines and has an even greater impact on offshore wind systems, where the harsh environment and the high cost of the assets and logistics increase the importance of a proactive approach to the system’s maintenance. The smart management of an asset starts with the identification of the health status of its systems, to take cost effective decision on how and when maintain it. The first level of the detection of an anomality in the system comprises the recognition only of the failed status of the asset (level I). Following, the location of the failure should be identified (level II), followed by the detection of its degree of severity (level III) and consequences (level IV). Depending on the availability of continuous monitoring data, historical databases, and advanced numerical models, different frameworks can be established for the failure diagnostics and prognostics. This thesis investigates on the use data-driven, model-based, and digital twin solutions to support the diagnosis of failure events of offshore wind turbine systems characterised by a low availability of run-to-failure data. This topic is of major concern for either the current installations - for which the collection of data is restrained either to only few assets or to more cost-effective temporary monitoring campaign – and the new offshore wind technologies (e.g., floating wind, large-MW structures), for which no or only a limited amount of operating data has been gathered. The mechanical failure of the components of the offshore wind speed conversion system can have a significant impact to the operational expenditure and can be associated to a significant loss of production of the offshore wind farm. The detection of their incipiency has been extensively investigated by machine and deep learning techniques on big sets of condition monitoring and operational data. By contrast, this research explores the implementation of transfer learning to detect anomalies in an offshore wind gearbox with low availability of representative failure data. To move towards the quantification of the consequences of such a failure (level IV), a case of study is used to explore then most suitable the model-reduction techniques to be applied to a full aero-servo-elastic model of the offshore wind turbine. Such a numerical model is the basis for the development of digital twin technology; it is aimed at capturing the only the essential dynamics while targeting the degree(s) of freedom indicating the presence of the failure mode. The presence of a damage in the offshore wind foundation is not commonly recorded, yet structural failures can either lead to catastrophic consequence or considerably increase the cost of maintenance for the planning of expensive subsea inspections. In particular, the fatigue-driven offshore wind jacket foundation designs are sensitive to extreme site conditions, and their expected lifetime can decrease considerably if exposed for a long time to undetected phenomena such as scour and corrosion. This research demonstrates the feasibility of a vibration-based diagnosis (level II) of several damage scenarios for a jacket substructure of an offshore wind turbine. Considering than only a percentage of the assets in the farm are likely to be instrumented with a high-frequency structural health monitoring system, the feasibility of the detection (level I) of a structural failure mode via low-resolution operational data is additionally explored. These virtual monitoring frameworks are supported by the deployment of the digital twin technologies for their setup and their future field application

Dataset for European Installed Offshore Wind Turbines (until year end 2017)

<div><b><u>Introduction and aim</u></b></div><div>This dataset is aimed to list and collect the main characteristics of the European Offshore Wind Farms (to end of 2017). </div><div>Firstly, this work wants to update and extend the one started by Zhang et al. [1], who gathered the main information and identified the drivetrain types for some of offshore EU wind turbines’ installed, until the end of 2011.</div><div>Secondly, the wind turbines belonging to the population studied by Carroll et al. [2], [3] (in their reliability database), are identified and analysed more in details.</div><div><br></div><div><u><b>Dataset organisation</b></u></div><div>The dataset is organised in an Excel worksheet, consisting of:</div><div><b><i>sheet 1 </i>- “Legend”</b></div><div>Acronyms and colour coded legend are explained. Additionally the following acronyms are used in the Excel work and throughout this introduction:</div><div>- WT(s) = Wind Turbine(s)</div><div>- WF(s) = Wind Farm(s)</div><div><b><i>sheet 2</i> - "EU WFs”</b><br></div><div>Data from Zhang et al. [1] have been verified and updated by accessing the main information of the wind farms (see link in reference in the section). </div><div>In particular, for each project, the following information are reported:</div><div> - WF name, capacity and country</div><div> - number of WTs</div><div> - WTs manufacturer/type</div><div> - type of control, gearbox, generator, and converter</div><div> - year when WF was online</div><div> - average distance from shore</div><div> - current status of the WF</div><div><b><i>sheet 3 </i>- "EU WFs (Fully-Grid Connected)”</b><br></div><div>The fully-grid connected, and still in operation, wind farms are selected out of the ones listed in <i>sheet 1</i>. </div><div>In the main table (<i>Range(“A1:N83”)</i>), the WTs are identified in the four drivetrain types (and type D sub-types), defined by Perez et al. [4] (<i>N2:N83</i>). </div><div>A table reporting the acronyms for the “if” cycle on the WT characteristics (speed, gearbox and generator) is reported in cells <i>Range(“AH2:AL11”)</i>.</div><div>Based on this latter, cells in <i>Range(“Q1:AD84”)</i> contain “if” cycles for identifying the share of each drivetrain type on the total installed capacity. The results are plotted in a pie chart, gathering type A and B. </div><div>Finally, the table in Range<i>(“AS1:CA86”)</i> wants to verify how much of the actual installed (fully-grid connected) capacity is accounted in this dataset. WindEurope report on offshore wind energy statistics, to the end of 2017 [5], is used as a reference, and the sharing to the total capacity of the several manufacturers and of the top 5 countries and is checked.</div><div><b><i>sheet 4 - </i>“Strath. Stats (population info)”</b><br></div><div>For a deeper understanding of the population analysed by Carroll et al. [2], the WTs with the following characteristics have been outlined (by the use of “if” cycles on the main table of <i>sheet 2</i>): </div><div> - at least 3 year old structure (in 2016)</div><div> - geared WTs with an induction machine (either SGIG, WRIG or DFIG)</div><div>Among these, structures between 3 and 5 years old and above 5 years old are distinguished as done by the reference.</div><div><br></div><div><b><u>References</u></b></div><div><b>[1]</b>Z. Zhang, A. Matveev, S. Øvrebø, R. Nilssen, and A. Nysveen, “State of the art in generator technology for offshore wind energy conversion systems,” in 2011 IEEE International Electric Machines & Drives Conference (IEMDC), 2011, pp. 1131–1136.</div><div><b>[2]</b>J. Carroll, A. McDonald, and D. McMillan, “Failure rate, repair time and unscheduled O&M cost analysis of offshore wind turbines,” Wind Energy, vol. 19, pp. 1107–1119, 2016.</div><div><b>[3]</b>J. Carroll, A. McDonald, I. Dinwoodie, D. McMillan, M. Revie, and I. Lazakis, “Availability, operation and maintenance costs of offshore wind turbines with different drive train configuration,” Wind Energy, vol. 20, no. July 2016, pp. 361–378, 2017.</div><div><b>[4]</b>J. M. Pinar Pérez, F. P. García Márquez, A. Tobias, and M. Papaelias, “Wind turbine reliability analysis,” Renew. Sustain. Energy Rev., vol. 23, pp. 463–472, 2013.</div><div><b>[5]</b>WindEurope, “Offshore wind in Europe: Key trends and statistics 2017,” 2018.</div><div><br></div><div> </div><div>The links below were used to extract the majority of the information about the wind farms and their wind turbines, respectively.</div><div><b>https://www.4coffshore.com/windfarms/<br></b></div><div><b>https://en.wind-turbine-models.com/turbines</b><br></div><div>Moreover, for these latter, a .zip folder with additional open access information (collected from various sources) is uploaded.</div

On the comparison of the dynamic response of an offshore floating VAWT system when adopting two different mooring system model of dynamics: quasi-static vs lumped mass approach

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-hydromooring coupled model of dynamics for VAWT \u201cFloVAWT\u201d [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 lumpedmass mooring line model \u201cMoorDyn\u201d [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. 1 Previous affiliation: University of Main