When wind travels through turbines: A new statistical approach for characterizing heterogeneous wake effects in multi-turbine wind farms

You, Mingdi; Byon, Eunshin; Jin, Jesse S.; Lee, Giwhyun

doi:10.1080/0740817x.2016.1204489

Cited by 26 publications

(10 citation statements)

References 29 publications

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“…Future studies extend the proposed approach to the extreme load estimation considering wake effects in a multi-turbine wind farm. 11…”

Section: Discussionmentioning

confidence: 99%

“…In this case study, we investigate two types of load responses, namely, the flapwise bending moment and the in-plane tip deflection. To obtain the IS density q X (x; θ) in (9), we need s(x; θ) in (11). We take a pilot sample and fit the non-homogeneous GEV distribution with the output sample data and approximate s(x, θ).…”

Section: Wind Turbine Extreme Load Estimationmentioning

confidence: 99%

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Adaptive Extreme Load Estimation in Wind Turbines

Pan

Byon²

2017

35th Wind Energy Symposium

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To help wind turbine reliability analysis in a design stage, aeroelastic simulators have been developed to generate stochastic load responses imposed on a wind turbine structure, given pre-specified turbulent wind conditions. In particular, system designers can use the simulators to estimate the extreme load responses during a turbine's design life. However, it has been shown that using the crude Monte Carlo sampling methods to simulate the extreme load associated with a small load exceeding probability is computationally prohibitive, and the estimation results are highly uncertain. Importance sampling methods can overcome the limitations of the crude Monte Carlo sampling methods. We develop adaptive algorithms to iteratively refine the importance sampling density to efficiently estimate the extreme load. Nomenclature P T Target probability of load exceedance l T Extreme load X Input vector Y Output variable p X Original input density of X q X Importance sampling density θ Parameter in the importance sampling density

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“…Future studies extend the proposed approach to the extreme load estimation considering wake effects in a multi-turbine wind farm. 11…”

Section: Discussionmentioning

confidence: 99%

Section: Wind Turbine Extreme Load Estimationmentioning

confidence: 99%

Adaptive Extreme Load Estimation in Wind Turbines

Pan

Byon²

2017

35th Wind Energy Symposium

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“…To forecast future wind power generation, the predicted wind speed should be converted to the wind power prediction through the power curve. Studies in the literature estimate the power curve using various methods such as polynomial regression, splines and nonparametric models, neural-networks and support vector machines [6], [16], [17], [18]. Once the power curve is constructed, future wind power outputs are typically predicted by plugging the wind speed forecast to the power curve function.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrative Density Forecast and Uncertainty Quantification of Wind Power Generation

Wang

AlShelahi

You

et al. 2021

IEEE Trans. Sustain. Energy

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We develop an integrative framework to predict the wind power output, considering many uncertainties. For probabilistic wind power forecasts, all the sources of uncertainties arising from both wind speed prediction and wind-to-power conversion process should be collectively addressed. To this end, we model the wind speed using the inhomogeneous geometric Brownian motion and convert the wind speed's prediction density into the wind power density in a closed-form. The resulting wind power density allows us to quantify prediction uncertainties through prediction intervals and to forecast the power that can minimize the expected prediction cost with unequal penalties on the overestimation and underestimation. We evaluate the predictive power of the proposed approach using data from commercial wind farms located in different sites. The results suggest that our approach outperforms alternative approaches in terms of multiple performance measures.

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“…Wind energy is one of the fastest growing renewable energy sources (You et al, 2017). Yet, harvesting wind energy remains expensive, compared with fossil energy sources such as oil, coal, and natural gas, due to the high capital cost in installing wind turbines.…”

Section: Case Studymentioning

confidence: 99%

Importance sampling and its optimality for stochastic simulation models

Chen

Choe

2019

Electron. J. Statist.

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We consider the problem of estimating an expected outcome from a stochastic simulation model. Our goal is to develop a theoretical framework on importance sampling for such estimation. By investigating the variance of an importance sampling estimator, we propose a two-stage procedure that involves a regression stage and a sampling stage to construct the final estimator. We introduce a parametric and a nonparametric regression estimator in the first stage and study how the allocation between the two stages affects the performance of the final estimator. We analyze the variance reduction rates and derive oracle properties of both methods. We evaluate the empirical performances of the methods using two numerical examples and a case study on wind turbine reliability evaluation.MSC 2010 subject classifications: Primary 62G20; secondary 62G86, 62H30.

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When wind travels through turbines: A new statistical approach for characterizing heterogeneous wake effects in multi-turbine wind farms

Cited by 26 publications

References 29 publications

Adaptive Extreme Load Estimation in Wind Turbines

Adaptive Extreme Load Estimation in Wind Turbines

Integrative Density Forecast and Uncertainty Quantification of Wind Power Generation

Importance sampling and its optimality for stochastic simulation models

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