Availability, operation and maintenance costs of offshore wind turbines with different drive train configurations

Carroll, J. Douglas; McDonald, Alasdair; Dinwoodie, Iain; McMillan, David; Revie, Matthew; Lazakis, Iraklis

doi:10.1002/we.2011

Cited by 119 publications

(110 citation statements)

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“…Thus only failure rates can be compared [52]. Further publications [53,54] of Carroll et al also make use of the described data source and furthermore include results on onshore WT. These publications focus on differences between drive train concepts and are thus not considered in the present comparison.…”

Section: Muppandal Wind Farm (India)mentioning

confidence: 99%

Performance and Reliability of Wind Turbines: A Review

2017

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Performance (availability and yield) and reliability of wind turbines can make the difference between success and failure of wind farm projects and these factors are vital to decrease the cost of energy. During the last years, several initiatives started to gather data on the performance and reliability of wind turbines on-and offshore and published findings in different journals and conferences. Even though the scopes of the different initiatives are similar, every initiative follows a different approach and results are therefore difficult to compare. The present paper faces this issue, collects results of different initiatives and harmonizes the results. A short description and assessment of every considered data source is provided. To enable this comparison, the existing reliability characteristics are mapped to a system structure according to the Reference Designation System for Power Plants (RDS-PP R ). The review shows a wide variation in the performance and reliability metrics of the individual initiatives. Especially the comparison on onshore wind turbines reveals significant differences between the results. Only a few publications are available on offshore wind turbines and the results show an increasing performance and reliability of offshore wind turbines since the first offshore wind farms were erected and monitored.

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Section: Muppandal Wind Farm (India)mentioning

confidence: 99%

Performance and Reliability of Wind Turbines: A Review

2017

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“…Multiple factors impact the IAV of net AEP from operating wind farms including but not limited to curtailment for system operation and/or WT maintenance (Clifton et al, 2016), WT wake losses (Clifton et al, 2016;Barthelmie et al, 2013), and 10 wind speed variability. Here we focus on this last factor.…”

Section: Simulationsmentioning

confidence: 99%

“…Onshore availability typically exceeds 98% (Carroll et al, 2017), but tends to decrease with WT age (Olauson et al, 2017). Thus gross CF derived from the WRF simulations that assume 100% WT availability (i.e.…”

Section: 2018) Annual Gross Capacity Factors (Cf) For Grid 15mentioning

confidence: 99%

“…25 Accurate quantification of the wind resource and the P50(AEP) and P90(AEP) presents a significant challenge to current models (Zhang et al, 2015), and even small uncertainties in modelled wind speeds cause major uncertainties in P50 (AEP) and P90(AEP) and significantly impact the cost of investment capital in new wind projects (Tindal, 2011;Clifton et al, 2016). Capital investments by the wind energy industry within the United States of America during 2016 are estimated at $14.5 billion (Dykes et al, 2017), while estimates of investment in the European offshore wind energy are projected to be 30 between $90-124 billion over the period 2013-2020(Gatzert and Kosub, 2016.…”

Section: Introductionmentioning

confidence: 99%

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Inter-annual variability of wind climates and wind turbine annual energy production

Pryor¹,

Shepherd²,

Barthelmie³

2018

Preprint

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Abstract.Inter-annual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in pre-construction projected AEP and the difference in 50 th and 90 th percentile (P50 and P90) AEP derives in part from variability in wind climates. However, the magnitude of IAV in wind speeds at/close to wind 10 turbine hub-heights is poorly constrained and maybe overestimated by the 6% standard deviation of annual mean wind speeds that is widely applied within the wind energy industry. Thus there is a need for improved understanding of the longterm wind resource and the inter-annual variability therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub-heights over the eastern USA indicate median gross capacity factors (computed using 10-minute wind speeds close to wind turbine hub-heights and the 15 power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at/near to typical wind turbine hub-heights in these simulations is lower than is implied by assuming a standard deviation of 6%. Indeed, rather than in 9 in 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP, results presented herein indicate that over 90% of the area in the eastern USA that currently has operating wind turbines simulated AEP lies within 0.94 and 1.06 of the long-term average. 20Further, IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to pre-construction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

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“…Based on [27] and [28], for a 6MW wind turbine with a 90m hub height in 30m water depth, the relationship of substructure and foundation mass, msf with top head mass can be given as (20) Using the steel cost from Table III (assuming 60% pile and 40% transition piece and outfitting steel), The effect of any increased generator mass on the turbine substructure and foundation cost, Csf (k€) can be approximated by a linear function for 400 < mtop < 600 tonnes as, (21) E. Optimization Design optimization methods generally use an algorithm which take independent variables as input and vary those input to evaluate dependent variables in machine model and hence optimize an objective function (subject to predetermined constraints). In this paper, the independent variables and constraints are described in the following subsection 1.…”

Section: Tower and Foundation Costmentioning

confidence: 99%

On the Optimization of Generators for Offshore Direct Drive Wind Turbines

McDonald

Bhuiyan

2017

IEEE Trans. Energy Convers.

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The Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output.1 Abstract-The objective of this paper is to optimize direct drive permanent magnet synchronous generators for offshore direct drive wind turbines in order to reduce the cost of energy. A 6MW wind turbine design is assumed and parametric electromagnetic and structural generator models are introduced for a surface-mounted magnet generator topology (using magnets with high BHmax) and a flux-concentrating variant (using magnets with lower BHmax). These are optimized using a hybrid Genetic Algorithm and Pattern Search process and the results show that the surface-mounted permanent magnet generator produces the lower cost of energy. The choice of objective function is addressed and it is found that a simplified metric incorporating generator cost and losses proxy produces similar designs to a full cost of energy calculation. Further steps to improve the quality of the model include the effect of generator mass on the design and cost of the turbine tower and foundation, which can add €0.4m to the turbine cost. Further optimizations are carried out to show the impacts of magnetic material costs (doubling this leads to a €1.1/MWh increase in cost of energy) and generator diameter limits (increasing the upper limit from 6m to 8m leads to a 0.9% drop in cost of energy) have on the choice of optimum independent variables.Index Terms-Cost of energy, direct drive wind turbine, optimization, permanent magnet generator, structural model, tower and foundation. I. INTRODUCTIONgrowing proportion of offshore wind turbine designs are now based on directly driven permanent magnet synchronous generators. Direct drive machines can offer higher reliability and reduced maintenance cost because of the omission of the gearbox from the drive train [1]. Some of the downsides of these generators include their large size (due to the high torque rating), requirements for large quantities of rare earth permanent magnets and the significant generator structures required to maintain the small air-gap clearance against the large attraction forces between the rotor and the stator [2]. The generator designer needs to deliver a number of performance characteristics including high efficiency, low power losses at part load, high availability, low machine mass, reduced volume and lower material and manufacturing costs. Normally the designers employ some element of optimization to achieve the best balance of these aspects [3].The main purpose of this paper is to examine the process of optimizing large, low speed generators for offshore direct drive wind turbines, exploring the different objective functions that a A. McDonald and N. A. Bhuiyan are with the Institute of Energy and Environment, Department of Electronic and Electri...

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Availability, operation and maintenance costs of offshore wind turbines with different drive train configurations

Cited by 119 publications

References 17 publications

Performance and Reliability of Wind Turbines: A Review

Performance and Reliability of Wind Turbines: A Review

Inter-annual variability of wind climates and wind turbine annual energy production

On the Optimization of Generators for Offshore Direct Drive Wind Turbines

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