scholarly journals Damage Equivalent Load Synthesis and Stochastic Extrapolation for Fatigue Life Validation

2021 ◽  
Author(s):  
Anand Natarajan
Keyword(s):  
Fatigue Life ◽  
Gaussian Process ◽  
Wind Farm ◽  
Time Duration ◽  
Level Crossings ◽  
Probability Of Exceedance ◽  
Stochastic Extrapolation ◽  
Blade Root ◽  
Equivalent Load

Abstract. Present verification of the fatigue life margins on wind turbine structures utilizes damage equivalent load (DEL) computations over limited time duration. In this article, a procedure to determine long term fatigue damage and remaining life is presented as a combination of stochastic extrapolation of the 10-minute DEL to determine its probability of exceedance and through computationally fast synthesis of DELs using level-crossings of a Gaussian process. Both the synthesis of DELs and long-term stochastic extrapolation are validated using measured loads from a wind farm. The extrapolation for the blade root flap and tower base fore-aft damage equivalent moment is presented using a three-parameter Weibull distribution, whereby the long term damage equivalent load levels are forecast for both simulated and measured values. The damage equivalent load magnitude at a selected target probability of exceedance provides an indicator of the integrity of the structure for the next year. The extrapolated damage equivalent load over a year is validated using measured multi-year damage equivalent loads from a turbine in the Lillgrund wind farm, which is subject to wakes. The simulation of damage equivalent loads using the method of level crossings of a Gaussian process is shown to be able to reconstruct the damage equivalent load for both blade root and tower base moments. The prediction of the tower base fore-aft DEL is demonstrated to be feasible when using the Vanmarcke correction for very-narrow band processes. The combined method of fast damage equivalent load computations and stochastic extrapolation to the next year, allows a quick and accurate forecasting of structural integrity of operational wind turbines.

scholarly journals Analysis of long-term temperature monitoring of multiple wind turbines

2021 ◽  
pp. 002029402110130
Author(s):  
Xian Wang ◽  
Qian-cheng Zhao ◽  
Xue-bing Yang ◽  
Bing Zeng
Keyword(s):  
Wind Turbines ◽  
Temperature Rise ◽  
Performance Optimization ◽  
Wind Farm ◽  
Engineering Practice ◽  
Multiple Perspectives ◽  
Temperature State ◽  
Condition Monitoring Systems ◽  
Historical Temperature

The historical temperature data logged in the supervisory control and data acquisition (SCADA) system contains a wealth of information that can assist with the performance optimization of wind turbines (WTs). However, mining and using these long-term data is difficult and time-consuming due to their complexity, volume, etc. In this study, we tracked and analyzed the 5-year trends of major SCADA temperature rise variables in relation to the active power of four WTs in a real wind farm. To uncover useful information, an extended version of the bins method, which calculates the standard deviation (SD) as well as the average, is proposed and adopted. The implications of the analysis for engineering practice are discussed from multiple perspectives. The research results demonstrate a change in the patterns of the main temperature rise variables in a real wind farm, completeness of the monitoring of the WT internal temperature state, influence of wind turbine aging on temperature signals, a correlation between different measurement points, and a correlation between signals from different years. The knowledge gained from this research provides a reference for the development of more practical and comprehensive condition monitoring systems and methods, as well as better operation maintenance strategies.

scholarly journals GMM-HMM-based Medium- and Long-term Multi-Wind Farm Correlated Power Output Time Series Generation Method

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yufei Li ◽  
Bo Hu ◽  
Tao Niu ◽  
Shengpu Gao ◽  
Jiahao Yan ◽  
...  
Keyword(s):  
Time Series ◽  
Power Output ◽  
Wind Farm ◽  
Output Time

scholarly journals Structural and mechanical defects of materials of offshore and onshore main gas pipelines after long-term operation

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Pavlo Maruschak ◽  
Sergey Panin ◽  
Iryna Danyliuk ◽  
Lyubomyr Poberezhnyi ◽  
Taras Pyrig ◽  
...  
Keyword(s):  
Fatigue Crack ◽  
Fatigue Life ◽  
Quantitative Analysis ◽  
Stress Concentration ◽  
Cyclic Loading ◽  
Fatigue Cracks ◽  
Preliminary Deformation ◽  
Term Operation ◽  
Steel Pipes

AbstractThe study has established the main regularities of a fatigue failure of offshore gas steel pipes installed using S-lay and J-lay methods.We have numerically analyzed the influence of preliminary deformation on the fatigue life of 09Mn2Si steel at different amplitudes of cyclic loading. The results have revealed the regularities of formation and development of a fatigue crack in 17Mn1Si steel after 40 years of underground operation. The quantitative analysis describes the regularities of occurrence and growth of fatigue cracks in the presence of a stress concentration.

Evaluating the Coupledness of the Aerodynamics and Hydrodynamics on the Estimation of Fatigue Damage Equivalent Load for a Floating Offshore Wind Platform

2018 ◽  
Author(s):  
Samuel Kanner ◽  
Bingbin Yu
Keyword(s):  
Fatigue Life ◽  
Fatigue Damage ◽  
Environmental Conditions ◽  
Offshore Wind ◽  
Coupled Analysis ◽  
Offshore Wind Turbine ◽  
Wave Conditions ◽  
Equivalent Load ◽  
The Waves

In this research, the estimation of the fatigue life of a semi-submersible floating offshore wind platform is considered. In order to accurately estimate the fatigue life of a platform, coupled aerodynamic-hydrodynamic simulations are performed to obtain dynamic stress values. The simulations are performed at a multitude of representative environmental states, or “bins,” which can mimic the conditions the structure may endure at a given site, per ABS Floating Offshore Wind Turbine Installation guidelines. To accurately represent the variety of wind and wave conditions, the number of environmental states can be of the order of 103. Unlike other offshore structures, both the wind and wave conditions must be accounted for, which are generally considered independent parameters, drastically increasing the number of states. The stress timeseries from these simulations can be used to estimate the damage at a particular location on the structure by using commonly accepted methods, such as the rainflow counting algorithm. The damage due to either the winds or the waves can be estimated by using a frequency decomposition of the stress timeseries. In this paper, a similar decoupled approach is used to attempt to recover the damages induced from these coupled simulations. Although it is well-known that a coupled, aero-hydro analysis is necessary in order to accurately simulate the nonlinear rigid-body motions of the platform, it is less clear if the same statement could be made about the fatigue properties of the platform. In one approach, the fatigue damage equivalent load is calculated independently from both scatter diagrams of the waves and a rose diagram of the wind. De-coupled simulations are performed to estimate the response at an all-encompassing range of environmental conditions. A database of responses based on these environmental conditions is constructed. The likelihood of occurrence at a case-study site is used to compare the damage equivalent from the coupled simulations. The OC5 platform in the Borssele wind farm zone is used as a case-study and the damage equivalent load from the de-coupled methods are compared to those from the coupled analysis in order to assess these methodologies.

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

2018 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie
Keyword(s):  
Standard Deviation ◽  
Wind Energy ◽  
Wind Turbine ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Speeds ◽  
Annual Variability ◽  
Eastern Usa

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 50th and 90th 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 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 long-term 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 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. Further, 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.

scholarly journals Effect of Carbon Dioxide Concentration on Cereal Yield in Sudan

2019 ◽  
Vol 5 ◽  
pp. 1
Author(s):  
Ibrahim A. Onour ◽  
Keyword(s):  
Carbon Dioxide ◽  
Positive Impact ◽  
Carbon Dioxide Concentration ◽  
Cointegration Analysis ◽  
Short Term ◽  
Time Duration ◽  
Yield Increase ◽  
Research Findings ◽  
Bound Test

To estimate the long-term effect of carbon dioxide (CO2) emission on cereal yield in Sudan, we employed an autoregressive distributed lagged (ARDL) bound test for cointegration analysis. The ARDL results reveal evidence of cointegration between the dependent variable (cereals yield) and two independent variables (CO2 emission) and agricultural GDP. The estimation results of the error correction model indicate that change in CO2 has a positive and significant impact on the cereal yield in the long and short terms, as 1% increase in CO2 leads to a cereal yield increase by 3% in the short term and by 0.7% in the long term. This result adds two important findings to the existing literature: First, the positive impact of CO2 on cereal yield in Sudan supports previous research findings in other countries of warm and arid climates. Second, the effect of CO2 on cereal yield differs from short to long term, as our finding indicates that CO2 has a greater positive effect in the short term compared to that in the long term, implying that the effect of CO2 on cereal yields is not linear, as commonly perceived, but it decreases as time duration extends to longer periods. This may be due to the CO2 effect on global warming that emanates from cumulative CO2 concentration, which leaves a disproportionate impact on crops over time.

Long-Term Wind Power Prediction Based on Rough Set

2013 ◽  
Vol 329 ◽  
pp. 411-415 ◽  
Author(s):  
Shuang Gao ◽  
Lei Dong ◽  
Xiao Zhong Liao ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Rough Set ◽  
Wind Farm ◽  
Rough Set Theory ◽  
Prediction Method ◽  
Key Factors ◽  
Power Prediction ◽  
Wind Power Prediction

In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

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