scholarly journals Assessing offshore wind power resources in British Columbia

2020 ◽  
Author(s):  
◽  
Talaat Bakri
Keyword(s):  
Power Generation ◽  
British Columbia ◽  
Wind Speed ◽  
Wind Power ◽  
Offshore Wind ◽  
Wind Fields ◽  
Wind Variability ◽  
Extreme Wind ◽  
Extreme Wind Speed

Wind resources are investigated and estimated offshore of the northern and central coasts of British Columbia, Canada. Remote sensing-based wind speed observations from a Synthetic Aperture Radar (SAR) mounted on the Canadian RADARSAT-2 satellite are used for mapping offshore winds. In addition, in-situ wind speed observations extracted from several buoys distributed in the study region are used to analyze the temporal and spatial wind speed variations in relation to wind power generation. Sustained winds above several wind turbine thresholds are analyzed and values of 50-yr and 100-yr return extreme wind speed levels are calculated. The wind variability analysis suggests few interruptions to power generation by either very low wind speeds or extreme wind speed events with high spatial variability between offshore areas and sites located within the coastal mountains. The SAR wind speed fields are characterized by a high spatial resolution but cover a period of less than 2.5 years with a random temporal availability. The SAR fields are extrapolated to reanalysis long-term wind fields that are available over a climatological time period with a sub-daily temporal resolution but a coarse spatial resolution. The extrapolation procedure is developed by applying a statistical downscaling model and a bias-based correction method. Wind fields from both methods are validated against the in-situ observations from buoys. The extrapolated wind fields are used for mapping offshore winds by creating a robust wind climatology that represents the mesoscale wind variance as well as the diurnal wind variability. This wind climatology is used to calculate the wind statistics and power density, in addition to estimate offshore wind resources. Viable areas for wind power development are defined by using high resolution bathymetric data and considering the general environmental and ecological constraints in the region. The estimated offshore wind resource energy using only theiv determined viable areas is found to resemble a large portion of the current total power generation in British Columbia. Most suitable areas for offshore wind farms are determined by developing criteria based on a combination of the turbine tower technology, water depth zoning and power density values.

Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning

2020 ◽  
Author(s):  
Do-Eun Choe ◽  
Gary Talor ◽  
Changkyu Kim
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Weather Data ◽  
Local Wind ◽  
Offshore Wind Power ◽  
The Future

Abstract Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size.

Analysis of Extreme Wind Speed Estimates in the Northern South China Sea

2020 ◽  
Vol 59 (10) ◽  
pp. 1625-1635
Author(s):  
Zhiduo Yan ◽  
Liang Pang ◽  
Sheng Dong
Keyword(s):  
Parameter Estimation ◽  
Wind Speed ◽  
South China Sea ◽  
South China ◽  
Northern South China Sea ◽  
Distribution Model ◽  
Wind Fields ◽  
China Sea ◽  
Extreme Wind ◽  
Extreme Wind Speed

AbstractAn increasing number of coastal and offshore structures have been built for coastal protection and marine development in recent years, and these marine structures need to be reasonably designed on the basis of wind speed. In this paper, extreme wind speed estimates are studied in detail by using the best-track datasets of northwestern Pacific Ocean tropical cyclones and ERA5 wind field data. The extreme wind speed fits by five distributions are compared using a blended sample of the wind fields from the ERA5 dataset and parametric wind data. The blend of wind fields improved the data accuracy and extreme value estimation reliability. In addition, the effects of the distribution model, data, threshold, and parameter estimation methods on the calculated results are discussed. The results show that the data had the greatest influences on probability prediction, followed by the distribution model and the parameter estimation method, with the threshold presenting the least influence. In this study, the reliability of the estimates was improved and the uncertainty of the results was analyzed, and the findings provide a wind speed design reference for the northern South China Sea.

scholarly journals Prediction of Extreme Wind Speed for Offshore Wind Farms Considering Parametrization of Surface Roughness

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1033
Author(s):  
Xinwen Ma ◽  
Yan Chen ◽  
Wenwu Yi ◽  
Zedong Wang
Keyword(s):  
Surface Roughness ◽  
Wind Speed ◽  
Wind Field ◽  
Wind Farms ◽  
Sea Surface ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
Sea Surface Roughness ◽  
Extreme Wind ◽  
Extreme Wind Speed

Large-scale offshore wind farms (OWF) are under construction along the southeastern coast of China, an area with a high typhoon incidence. Measured data and typhoon simulation model are used to improve the reliability of extreme wind speed (EWS) forecasts for OWF affected by typhoons in this paper. Firstly, a 70-year historical typhoon record database is statistically analyzed to fit the typhoon parameters probability distribution functions, which is used to sample key parameters when employing Monte Carlo Simulation (MCS). The sampled typhoon parameters are put into the Yan Meng (YM) wind field to generate massive virtual typhoon in the MCS. Secondly, when typhoon simulation carried out, the change in wind field roughness caused by the wind-wave coupling is studied. A simplified calculation method for realizing this phenomenon is applied by exchanging roughness length in the parametric wind field and wave model. Finally, the extreme value theory is adopted to analyze the simulated typhoon wind data, and results are verified using measured data and relevant standards codes. The EWS with 50-year recurrence of six representative OWF is predicted as application examples. The results show that the offshore EWS is generally stronger than onshore; the reason is sea surface roughness will not keep growing accordingly as the wind speed increases. The traditional prediction method does not consider this phenomenon, causing it to overestimate the sea surface roughness, and as a result, underestimate the EWS for OWF affected by typhoons. This paper’s methods make the prediction of EWS for OWF more precise, and results suggest the planer should choose stronger wind turbine in typhoon prone areas.

scholarly journals A Reliability Based Model for Wind Turbine Selection

2013 ◽  
Vol 2 (2) ◽  
pp. 69-74 ◽  
Author(s):  
A.K. Rajeevan ◽  
P.V. Shouri ◽  
Usha Nair
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Point Of View ◽  
Cube Root ◽  
Mathematical Relationship ◽  
Turbine Generator ◽  
Wind Turbine Generator ◽  
A Site

A wind turbine generator output at a specific site depends on many factors, particularly cut- in, rated and cut-out wind speed parameters. Hence power output varies from turbine to turbine. The objective of this paper is to develop a mathematical relationship between reliability and wind power generation. The analytical computation of monthly wind power is obtained from weibull statistical model using cubic mean cube root of wind speed. Reliability calculation is based on failure probability analysis. There are many different types of wind turbinescommercially available in the market. From reliability point of view, to get optimum reliability in power generation, it is desirable to select a wind turbine generator which is best suited for a site. The mathematical relationship developed in this paper can be used for site-matching turbine selection in reliability point of view.

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