Experimental study on application of nacelle-mounted LiDAR for analyzing wind turbine wake effects by distance

Shin, Dongheon; Ko, Kyungnam

doi:10.1016/j.energy.2021.123088

Cited by 18 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Empirical research and computational modeling have been invaluable in extending our understanding of wind turbine wake aerodynamics. Real-world measurements of wake effects using advanced technologies such as LIDAR and SODAR provide critical insights into wake behavior under varying operational and environmental conditions [6,19,[28][29][30]. Concurrently, computational studies utilizing methods such as Computational Fluid Dynamics (CFD) and Large Eddy Simulation (LES) have provided a platform to simulate and study wake dynamics in controlled conditions, enabling researchers to isolate and understand the influence of specific factors [8,31,32].…”

Section: Wind Turbine Wake Aerodynamicsmentioning

confidence: 99%

Machine Learning-Based Approach to Wind Turbine Wake Prediction under Yawed Conditions

Gajendran,

Kabir,

Vadivelu

et al. 2023

JMSE

View full text Add to dashboard Cite

As wind energy continues to be a crucial part of sustainable power generation, the need for precise and efficient modeling of wind turbines, especially under yawed conditions, becomes increasingly significant. Addressing this, the current study introduces a machine learning-based symbolic regression approach for elucidating wake dynamics. Utilizing WindSE’s actuator line method (ALM) and Large Eddy Simulation (LES), we model an NREL 5-MW wind turbine under yaw conditions ranging from no yaw to 40 degrees. Leveraging a hold-out validation strategy, the model achieves robust hyper-parameter optimization, resulting in high predictive accuracy. While the model demonstrates remarkable precision in predicting wake deflection and velocity deficit at both the wake center and hub height, it shows a slight deviation at low downstream distances, which is less critical to our focus on large wind farm design. Nonetheless, our approach sets the stage for advancements in academic research and practical applications in the wind energy sector by providing an accurate and computationally efficient tool for wind farm optimization. This study establishes a new standard, filling a significant gap in the literature on the application of machine learning-based wake models for wind turbine yaw wake prediction.

show abstract

Section: Wind Turbine Wake Aerodynamicsmentioning

confidence: 99%

Machine Learning-Based Approach to Wind Turbine Wake Prediction under Yawed Conditions

Gajendran,

Kabir,

Vadivelu

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…The relatively scarce literature on the near wake is not only due to the complexity of the flow in that region, but also due to the difficulty of rear-facing NMLs in measuring at close ranges due to the blind region of lidars and high geometrical error. Shin and Ko (2022) attempted to overcome these limitations by measuring the near wake of a 3-MW turbine using a forward-facing fixed-beam lidar located on the nacelle of a downstream turbine. Using an a priori estimate of the wake region based on the Jensen wake model Jensen (1983) to define a wake region for averaging, they showed the characteristic double-Gaussian shape of the velocity deficit in the near wake due to the nacelle jet and a decrease of the turbulence intensity in the wake region with increasing wind speeds.…”

Section: Wake Physics and Structurementioning

confidence: 99%

Characterization of wind turbine flow through nacelle-mounted lidars: a review

Letizia,

Brugger,

Bodini

et al. 2023

Front. Mech. Eng.

View full text Add to dashboard Cite

This article provides a comprehensive review of the most recent advances in the planning, execution, and analysis of inflow and wake measurements from nacelle-mounted wind Doppler lidars. Lidars installed on top of wind turbines provide a holistic view of the inflow and wake characteristics required to characterize and optimize wind turbine performance, carry out model validation and calibration, and aid in real-time control. The need to balance the enhanced capabilities and limitations of lidars compared to traditional anemometers inspired a broad variety of approaches for scan design and wind reconstruction, which we discuss in this review. We give particular emphasis to identifying common guidelines and gaps in the available literature with the aim of providing an exhaustive picture of the state-of-the-art techniques for reconstructing wind plant flow using nacelle-mounted lidars.

show abstract

“…The wake effect negatively affects turbines that are placed too close together. A critical factor in the design of offshore wind farms (OWFs) is the distance between wind turbines (WTs) [14].…”

Section: Introductionmentioning

confidence: 99%

Offshore Wind Farm Layout Optimisation Considering Wake Effect and Power Losses

et al. 2023

View full text Add to dashboard Cite

The last two decades have witnessed a new paradigm in terms of electrical energy production. The production of electricity from renewable sources has come to play a leading role, thus allowing us not only to face the global increase in energy consumption, but also to achieve the objectives of decarbonising the economies of several countries. In this scenario, where onshore wind energy is practically exhausted, several countries are betting on constructing offshore wind farms. Since all the costs involved are higher when compared to onshore, optimising the efficiency of this type of infrastructure as much as possible is essential. The main aim of this paper was to develop an optimisation model to find the best wind turbine locations for offshore wind farms and to obtain the wind farm layout to maximise the profit, avoiding cable crossings, taking into account the wake effect and power losses. The ideal positioning of wind turbines is important for maximising the production of electrical energy. Furthermore, a techno-economic analysis was performed to calculate the main economic indicators, namely the net present value, the internal rate of return, and the payback period, to support the decision-making. The results showed that the developed model found the best solution that maximised the profits of the wind farm during its lifetime. It also showed that the location of the offshore substation played a key role in achieving these goals.

show abstract

Experimental study on application of nacelle-mounted LiDAR for analyzing wind turbine wake effects by distance

Cited by 18 publications

References 12 publications

Machine Learning-Based Approach to Wind Turbine Wake Prediction under Yawed Conditions

Machine Learning-Based Approach to Wind Turbine Wake Prediction under Yawed Conditions

Characterization of wind turbine flow through nacelle-mounted lidars: a review

Offshore Wind Farm Layout Optimisation Considering Wake Effect and Power Losses

Contact Info

Product

Resources

About