Wind turbine performance decline in Sweden

Olauson, Jon; Edström, Per; Rydén, Jesper

doi:10.1002/we.2132

Cited by 44 publications

(46 citation statements)

References 6 publications

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“…Allowing the gearbox to run to failure is indicated to be economically justified, showing that the gearbox has been quite robust and not the dominant factor in performance degradation. Finally, the results of the present work about wind turbine aging are more in line with those in [12] than in [14]: actually, if one extrapolates the present results to a twenty years period, the estimate agrees with [12]. It should be noted that, differently with respect to what observed in [12], the results of this study do not conform well to the hypothesis that the aging degradation is linear in time: actually, up to 7 years after the reference data set, the performance worsening is estimated to be of the order of 1%, while 10 years later the worsening reaches the 5%.…”

Section: Discussionsupporting

confidence: 84%

“…Another important consideration from the study in [12] is that this order of magnitude for the performance decline is remarkable but it is not disastrous because it is in line, for example, with gas turbine technology [13]. The study in [14] refers to wind turbines operating in Sweden, constructed before 2007. The methods and the results are different with respect to [12]: it arises that there is a 0.15 capacity factor percentage points per year decline, corresponding to a lifetime energy loss of 6%; this estimate is lower with respect to [12].…”

Section: Introductionmentioning

confidence: 99%

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A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis

et al. 2020

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Ageing of technical systems and machines is a matter of fact. It therefore does not come as a surprise that an energy conversion system such as a wind turbine, which in particular operates under non-stationary conditions, is subjected to performance decline with age. The present study presents an analysis of the performance deterioration with age of a Vestas V52 wind turbine, installed in 2005 at the Dundalk Institute of Technology campus in Ireland. The wind turbine has operated from October 2005 to October 2018 with its original gearbox, that has subsequently been replaced in 2019. Therefore, a key point of the present study is that operation data spanning over thirteen years have been analysed for estimating how the performance degrades in time. To this end, one of the most innovative approaches for wind turbine performance control and monitoring has been employed: a multivariate Support Vector Regression with Gaussian Kernel, whose target is the power output of the wind turbine. Once the model has been trained with a reference data set, the performance degradation is assessed by studying how the residuals between model estimates and measurements evolve. Furthermore, a power curve analysis through the binning method has been performed to estimate the Annual Energy Production variations and suggests that the most convenient strategy for the test case wind turbine (running the gearbox until its end of life) has indeed been adopted. Summarizing, the main results of the present study are as follows: over a ten-year period, the performance of the wind turbine has declined of the order of 5%; the performance deterioration seems to be nonlinear as years pass by; after the gearbox replacement, a fraction of performance deterioration has been recovered, though not all because the rest of the turbine system has been operating for thirteen years from its original state. Finally, it should be noted that the estimate of performance decline is basically consistent with the few results available in the literature.

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Section: Discussionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis

et al. 2020

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“…27 ). Availability of WT deployed onshore tends to decrease with WT age 41 but is typically ≥98% (ref. 42 ).…”

Section: Resultsmentioning

confidence: 99%

20% of US electricity from wind will have limited impacts on system efficiency and regional climate

Pryor

Barthelmie

Shepherd

2020

Sci Rep

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Impacts from current and future wind turbine (WT) deployments necessary to achieve 20% electricity from wind are analyzed using high resolution numerical simulations over the eastern USA. Theoretical scenarios for future deployments are based on repowering (i.e. replacing with higher capacity WTs) thus avoiding competition for land. Simulations for the contemporary climate and current WT deployments exhibit good agreement with observed electricity generation efficiency (gross capacity factors (CF) from simulations = 45-48%, while net CF for WT installed in 2016 = 42.5%). Under the scenario of quadrupled installed capacity there is a small decrease in system-wide efficiency as indicated by annual mean CF. This difference is approximately equal to that from the two simulation years and may reflect saturation of the wind resource in some areas. WT modify the local near-surface climate in the grid cells where they are deployed. The simulated impact on near-surface climate properties at both the regional and local scales does not increase with increasing WT installed capacity. Climate impacts from WT are modest compared to regional changes induced by historical changes in land cover and to the global temperature perturbation induced by use of coal to generate an equivalent amount of electricity. The imperative to transform the energy system is well documented and great strides have already been made to increase the penetration of low carbon sources into the global electricity supply 1,2. Over 340,000 wind turbines (WT) with a total capacity >591 GW were installed worldwide at the end of 2018 (ref. 3) with over 95 GW in the United States of America (USA) (ref. 4) (Fig. 1(a)). By 2015 WT supplied more than 4.3% of global electricity demand 5. The power in the wind scales wit h the area swept by the WT blades and is thus proportional to the rotor diameter squared (D 2). Wind speeds also generally increase with height. Thus, the total installed capacity (IC), average rated capacity and physical dimensions of WT being installed and wind generation of electricity have all exhibited marked growth in the USA over the last 20 years (Fig. 1(b)). The Central Plains exhibits the highest wind speeds and resource 6,7 and thus the highest density of current WT installations (Fig. 2). Wind generated electricity in the USA grew by 12% from 2016 to 2017 (from 227 to 254 TWh) and further increased to 275 TWh in 2018 (ref. 4) (Fig. 1(b)). Accordingly, WT provided 6.5% of total generation (4171 TWh, ref. 8) in 2018 (ref. 4). WT IC doubled between 2010 and 2017 and is projected to double again by 2021 (Fig. 1(b)). Quadrupling of installed WT capacity to meet a goal of 20% of USA electricity supply by 2030 (ref. 9) falls well below theoretical limits for potential wind generated electricity 10,11 , but implies approximately a further doubling of installed capacity after 2021. Here we address whether the expansion of WT IC in the eastern USA is likely to lead to either: (i) Decreased overall efficiency of electrical power production from the...

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“…Sixtyeight sites meet this data completeness criteria. It is important to note that application of these selection criteria is necessary 5 to ensuring the resulting IQR in CF estimates are robust, but it biases the resulting sample in two important ways; the overwhelming majority of these wind farms are located in Central Plains (Figure 3b) and they tend to represent older generation wind farms in which WT may no longer be under warrantee and may experience declining performance (Olauson et al, 2017), potential leading to inflation of IAV(CF). …”

Section: Observational Datamentioning

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%

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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Wind turbine performance decline in Sweden

Cited by 44 publications

References 6 publications

A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis

A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis

20% of US electricity from wind will have limited impacts on system efficiency and regional climate

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

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