Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications

Cevasco, Debora; Koukoura, Sofia; Kolios, Athanasios

doi:10.1016/j.rser.2020.110414

Cited by 94 publications

(41 citation statements)

References 37 publications

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“…Due to the sensitivity of this data most failure databases are anonymous and in some case normalised (Michael et al, 2011). Most publicly available sources summarised and compared in Cevasco et al (2021) contain failure data only for fixed wind turbines up to 2 MW in capacity and due to the differences in data collection, the results are often conflicting. Currently there is no database containing failure rates for floating wind turbines and their substructures, therefore this study utilised failure rates from oil & gas and ship industries discussed in Sect.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysing the effectiveness of different offshore maintenance base options for floating wind farms

Avanessova

Gray

Lazakis

et al. 2021

Preprint

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Abstract. With the growth of the floating wind industry, new Operation & Maintenance (O&M) research has emerged evaluating tow-to-port strategies (Offshore Wind Innovation Hub, 2020) but limited work has been done on analysing other logistical strategies for offshore floating wind farms. In particular, what logistical solutions are the best for farms located far offshore, that cannot be reached by crew transfer vessels (CTVs)? Previous studies have looked at the use of Surface Effect Ships (SES) and CTVs during the Operation and Maintenance (O&M) of fixed wind farms but only some of them included Service Operation Vessels (SOV). This study analyses two strategies that could be used for floating wind farms located far from shore using ORE Catapult’s in-house O&M simulation tool. One strategy comprises of having a SOV performing most of the maintenance on the wind farm and the other strategy uses an Offshore Maintenance Base (OMB) instead which would be located next to the offshore substation and would accommodate three CTVs. This paper provides an overview of the tool and the inputs used to run it, including failure rates of floating wind turbine subsea components and their replacement costs. In total six types of simulations were run with two strategies, two different weather limits for CTVs and two weather datasets ERA5 and ERA20C. The results of this study show that the Operational Expenditure (OPEX) costs for the strategy with an OMB are 5–8 % (depending on the inputs) lower that with SOV but if Capital Expenditure (CAPEX) costs are included in the analysis and the Net Present Value (NPV) is taken into account then the fixed costs associated with building the offshore maintenance base have a significant impact on selecting a preferred strategy. It was found that for the case study presented in this paper the OMB would have to share the foundation with a substation in order to be cost competitive with the SOV strategy.

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Section: Literature Reviewmentioning

confidence: 99%

Analysing the effectiveness of different offshore maintenance base options for floating wind farms

Avanessova

Gray

Lazakis

et al. 2021

Preprint

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“…Since the capture and exploitation of ocean energy started to receive attention by the scientific and industrial communities [3], the technologies for harnessing ocean energy have been investigated and developed significantly to meet the energy market, which considerably fosters the invention of diverse Ocean Energy Devices (OEDs) ranging from small-scale isolated apparatus [4] to largescale Integrated Energy Harvesting Systems (IEHSs) [5,6]. Generally speaking, as shown in Figure 1, the classification of OEDs can be mainly categorized by their energy resource, i.e., winds (e.g., Floating Wind Turbines, FWTs) [7][8][9][10], waves (e.g., Wave Energy Converters, WECs) [11][12][13], currents (e.g., Tidal Current Turbines, TCTs) [14][15][16], and multi-resource (see e.g., [6]). As pointed out by Said and Ringwood [17], ordinary OEDs consist of four phases to converting ocean energy to electricity (see also Figure 2), namely, absorption, transmission, generation, and conditioning.…”

Section: Introductionmentioning

confidence: 99%

A Review of SPH Techniques for Hydrodynamic Simulations of Ocean Energy Devices

et al. 2022

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This article is dedicated to providing a detailed review concerning the SPH-based hydrodynamic simulations for ocean energy devices (OEDs). Attention is particularly focused on three topics that are tightly related to the concerning field, covering (1) SPH-based numerical fluid tanks, (2) multi-physics SPH techniques towards simulating OEDs, and finally (3) computational efficiency and capacity. In addition, the striking challenges of the SPH method with respect to simulating OEDs are elaborated, and the future prospects of the SPH method for the concerning topics are also provided.

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“…The components of the drive-train have the potential highest impact on the maintenance cost of the next generation of offshore wind farms [3]. These are among the most expensive components for an offshore wind turbine, and are continuously undergoing remodelling where innovations are to accommodate bigger power outputs [4] with larger loads. Condition Monitoring (CM) signals, in combination with high-frequency Supervisory Control and Data Acquisition (SCADA) data have been extensively used to train machine learning (ML) models to predict failures in the drive-train components [11] [12].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Hard Parameter Multi-Task Learning for Condition Monitoring of an Offshore Wind Turbine

Black

Cevasco

Kolios

2022

J. Phys.: Conf. Ser.

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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.

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Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications

Cited by 94 publications

References 37 publications

Analysing the effectiveness of different offshore maintenance base options for floating wind farms

Analysing the effectiveness of different offshore maintenance base options for floating wind farms

A Review of SPH Techniques for Hydrodynamic Simulations of Ocean Energy Devices

Deep Neural Network Hard Parameter Multi-Task Learning for Condition Monitoring of an Offshore Wind Turbine

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