A new methodology for urban wind resource assessment

Simões, Tomás; Estanqueiro, Ana

doi:10.1016/j.renene.2015.12.008

Cited by 83 publications

(38 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Given the above background, the present study investigated the validity of a computational fluid dynamics (CFD)-based method for assessing the risk of wind turbine failures caused by terrain-induced turbulence at the planning stage of wind farm construction. CFD is a numerical simulation technique (software), which can simulate three-dimensional wind flow using computers [1][2][3][4][5][6][7][8][9][10][11][12]. The use of CFD software allows desktop assessment of wind flow for a potential wind farm prior to its actual construction.…”

Section: Introductionmentioning

confidence: 99%

A Large-Eddy Simulation-Based Assessment of the Risk of Wind Turbine Failures Due to Terrain-Induced Turbulence over a Wind Farm in Complex Terrain

Uchida

Takakuwa²

2019

Energies

View full text Add to dashboard Cite

The first part of the present study investigated the relationship among the number of yaw gear and motor failures and turbulence intensity (TI) at all the wind turbines under investigation with the use of in situ data. The investigation revealed that wind turbine #7 (T7), which experienced a large number of failures, was affected by terrain-induced turbulence with TI that exceeded the TI presumed for the wind turbine design class to which T7 belongs. Subsequently, a computational fluid dynamics (CFD) simulation was performed to examine if the abovementioned observed wind flow characteristics could be successfully simulated. The CFD software package that was used in the present study was RIAM-COMPACT, which was developed by the first author of the present paper. RIAM-COMPACT is a nonlinear, unsteady wind prediction model that uses large-eddy simulation (LES) for the turbulence model. RIAM-COMPACT is capable of simulating flow collision, separation, and reattachment and also various unsteady turbulence–eddy phenomena that are caused by flow collision, separation, and reattachment. A close examination of computer animations of the streamwise (x) wind velocity revealed the following findings: As we predicted, wind flow that was separated from a micro-topographical feature (micro-scale terrain undulations) upstream of T7 generated large vortices. These vortices were shed downstream in a nearly periodic manner, which in turn generated terrain-induced turbulence, affecting T7 directly. Finally, the temporal change of the streamwise (x) wind velocity (a non-dimensional quantity) at the hub-height of T7 in the period from 600 to 800 in non-dimensional time was re-scaled in such a way that the average value of the streamwise (x) wind velocity for this period was 8.0 m/s, and the results of the analysis of the re-scaled data were discussed. With the re-scaled full-scale streamwise wind velocity (m/s) data (total number of data points: approximately 50,000; time interval: 0.3 s), the time-averaged streamwise (x) wind velocity and TI were evaluated using a common statistical processing procedure adopted for in situ data. Specifically, 10-min moving averaging (number of sample data points: 1932) was performed on the re-scaled data. Comparisons of the evaluated TI values to the TI values from the normal turbulence model in IEC61400-1 Ed.3 (2005) revealed the following: Although the evaluated TI values were not as large as those observed in situ, some of the evaluated TI values exceeded the values for turbulence class A, suggesting that the influence of terrain-induced turbulence on the wind turbine was well simulated.

show abstract

Section: Introductionmentioning

confidence: 99%

A Large-Eddy Simulation-Based Assessment of the Risk of Wind Turbine Failures Due to Terrain-Induced Turbulence over a Wind Farm in Complex Terrain

Uchida

Takakuwa²

2019

Energies

View full text Add to dashboard Cite

show abstract

“…Electric power generation is usually carried out in large wind farms [7,8] far from urban centers [9,10], though, in the last few years, urban wind power generation is also gaining impulse [11], including its use in smart grids [12].…”

Section: Motivationmentioning

confidence: 99%

Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

et al. 2017

View full text Add to dashboard Cite

Wind Power Ramp Events (WPREs) are large fluctuations of wind power in a short time interval, which lead to strong, undesirable variations in the electric power produced by a wind farm. Its accurate prediction is important in the effort of efficiently integrating wind energy in the electric system, without affecting considerably its stability, robustness and resilience. In this paper, we tackle the problem of predicting WPREs by applying Machine Learning (ML) regression techniques. Our approach consists of using variables from atmospheric reanalysis data as predictive inputs for the learning machine, which opens the possibility of hybridizing numerical-physical weather models with ML techniques for WPREs prediction in real systems. Specifically, we have explored the feasibility of a number of state-of-the-art ML regression techniques, such as support vector regression, artificial neural networks (multi-layer perceptrons and extreme learning machines) and Gaussian processes to solve the problem. Furthermore, the ERA-Interim reanalysis from the European Center for Medium-Range Weather Forecasts is the one used in this paper because of its accuracy and high resolution (in both spatial and temporal domains). Aiming at validating the feasibility of our predicting approach, we have carried out an extensive experimental work using real data from three wind farms in Spain, discussing the performance of the different ML regression tested in this wind power ramp event prediction problem.

show abstract

“…Utmost caution is required, especially for wind power stations with complicated topography, in planning an optimum arrangement 2 of 27 of wind turbines and controlling both maintenance and management. Diversified tasks are increasingly important to avoid accumulated fatigue of wind load in wind turbines due to terrain-induced turbulence, to reduce malfunctions and accidents inside and outside wind turbines, and to improve the availability of wind turbines [1][2][3][4][5][6][7][8][9][10][11]. Considering social and engineering requests at present, the crucial purpose of the present study is to establish a system with a numerical diagnostic technique for wind status, which contributes to the proper operation of wind farms, an adequate understanding of the indigenous wind environments of each site, including terrain-induced turbulence, and a reduction of malfunctions and accidents associated with wind turbines [12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

New Assessment Scales for Evaluating the Degree of Risk of Wind Turbine Blade Damage Caused by Terrain-Induced Turbulence

Uchida

Kawashima²

2019

Energies

View full text Add to dashboard Cite

The present study scrutinized the impacts of terrain-induced turbulence on wind turbine blades, examining measurement data regarding wind conditions and the strains of wind turbine blades. Furthermore, we performed a high-resolution large-eddy simulation (LES) and identified the three-dimensional airflow structures of terrain-induced turbulence. Based on the LES results, we defined the Uchida-Kawashima Scale_1 (the U-K scale_1), which is a turbulence evaluation index, and clarified the existence of the terrain-induced turbulence quantitatively. The threshold value of the U-K scale_1 was determined as 0.2, and this index was confirmed to not be dependent on the inflow profile, the influence of the horizontal grid resolution, and the influence of the computed azimuth. In addition, we defined the Uchida-Kawashima Scale_2 (the U-K scale_2), which is a fatigue damage evaluation index based on the measurement data and the design value obtained by DNV GL’s Bladed. DNV GL (Det Norske Veritas Germanischer Lloyed) is a third party certification body in Norway, and Bladed has been the industry standard aero-elastic wind turbine modeling software. Using the U-K scale_2, the following results were revealed: the U-K scale_2 was 0.86 < 1.0 (within the designed value) in the case of northerly wind, and the U-K scale_2 was 1.60 > 1.0 (exceeding the designed value) in the case of easterly wind. As a result, it was revealed that the blades of the target wind turbine were directly and strongly affected by terrain-induced turbulence when easterly winds occurred.

show abstract

A new methodology for urban wind resource assessment

Cited by 83 publications

References 6 publications

A Large-Eddy Simulation-Based Assessment of the Risk of Wind Turbine Failures Due to Terrain-Induced Turbulence over a Wind Farm in Complex Terrain

A Large-Eddy Simulation-Based Assessment of the Risk of Wind Turbine Failures Due to Terrain-Induced Turbulence over a Wind Farm in Complex Terrain

Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

New Assessment Scales for Evaluating the Degree of Risk of Wind Turbine Blade Damage Caused by Terrain-Induced Turbulence

Contact Info

Product

Resources

About