Offshore wind turbine wake characteristics using scanning doppler lidar

Within an offshore wind park, wind flow characteristics are quite complex and govern both the energy production and the structural wind turbine response. An experimental study focussed on assessing the spatial variability of winds near the German offshore wind energy platform FINO1 was conducted using multiple remote sensing devices. This study focuses on measuring the wind turbine wake characteristics, such as velocity deficit, the extent (length and width) of the wake and wake meandering under various atmospheric conditions using the data collected from a single scanning Doppler Lidar for several months in 2016. A new algorithm based on using a Gaussian model to measure the downwind wake characteristics is developed. The wind turbine downwind wake deficits compared well to previous models at far-wake regions, while at near-wake regions the models deviated due to different instruments & methodologies used in measuring the wake characteristics. It was also observed that the length of the Alpha Ventus wind turbine wake varied from 3 to 15 times the Rotor Diameter (RD), and the maximum velocity deficit varied from 55% to 75% of the free-stream wind speed, depending on mean wind speed and atmospheric stability. Detailed analysis of the Alpha Ventus wind turbine wake characteristics is presented.publishedVersio

Impact of model improvements on 80 m wind speeds during the second Wind Forecast Improvement Project (WFIP2)

During the second Wind Forecast Improvement Project (WFIP2; October 2015&ndash;March 2017, held in the Columbia River Gorge and Basin area of eastern Washington and Oregon states), several improvements to the parameterizations used in the High Resolution Rapid Refresh (HRRR &ndash; 3&thinsp;km horizontal grid spacing) and the High Resolution Rapid Refresh Nest (HRRRNEST &ndash; 750&thinsp;m horizontal grid spacing) numerical weather prediction (NWP) models were tested during four 6-week reforecast periods (one for each season). For these tests the models were run in control (CNT) and experimental (EXP) configurations, with the EXP configuration including all the improved parameterizations. The impacts of the experimental parameterizations on the forecast of 80&thinsp;m wind speeds (wind turbine hub height) from the HRRR and HRRRNEST models are assessed, using observations collected by 19 sodars and three profiling lidars for comparison. Improvements due to the experimental physics (EXP vs. CNT runs) and those due to finer horizontal grid spacing (HRRRNEST vs. HRRR) and the combination of the two are compared, using standard bulk statistics such as mean absolute error (MAE) and mean bias error (bias). On average, the HRRR 80&thinsp;m wind speed MAE is reduced by 3&thinsp;%&ndash;4&thinsp;% due to the experimental physics. The impact of the finer horizontal grid spacing in the CNT runs also shows a positive improvement of 5&thinsp;% on MAE, which is particularly large at nighttime and during the morning transition. Lastly, the combined impact of the experimental physics and finer horizontal grid spacing produces larger improvements in the 80&thinsp;m wind speed MAE, up to 7&thinsp;%&ndash;8&thinsp;%. The improvements are evaluated as a function of the model's initialization time, forecast horizon, time of the day, season of the year, site elevation, and meteorological phenomena. Causes of model weaknesses are identified. Finally, bias correction methods are applied to the 80&thinsp;m wind speed model outputs to measure their impact on the improvements due to the removal of the systematic component of the errors. </div

Critical analysis of Big Data Challenges and analytical methods

Big Data (BD), with their potential to ascertain valued insights for enhanced decision-making process, have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable information from BD. The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals. However, there are different types of analytic applications to consider. Therefore, prior to hasty use and buying costly BD tools, there is a need for organizations to first understand the BDA landscape. Given the significant nature of the BD and BDA, this paper presents a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions. In doing so, systematically analysing and synthesizing the extant research published on BD and BDA area. More specifically, the authors seek to answer the following two principal questions: Q1 – What are the different types of BD challenges theorized/proposed/confronted by organizations? and Q2 – What are the different types of BDA methods theorized/proposed/employed to overcome BD challenges?. This systematic literature review (SLR) is carried out through observing and understanding the past trends and extant patterns/themes in the BDA research area, evaluating contributions, summarizing knowledge, thereby identifying limitations, implications and potential further research avenues to support the academic community in exploring research themes/patterns. Thus, to trace the implementation of BD strategies, a profiling method is employed to analyze articles (published in English-speaking peer-reviewed journals between 1996 and 2015) extracted from the Scopus database. The analysis presented in this paper has identified relevant BD research studies that have contributed both conceptually and empirically to the expansion and accrual of intellectual wealth to the BDA in technology and organizational resource management discipline

Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting

Although the random forest (RF) model is a powerful machine learning tool that has been utilized in many wind speed/power forecasting studies, there has been no consensus on optimal RF modeling strategies. This study investigates three basic questions which aim to assist in the discernment and quantification of the effects of individual model properties, namely: (1) using a standalone RF model versus using RF as a correction mechanism for the persistence approach, (2) utilizing a recursive versus direct multi-step forecasting strategy, and (3) training data availability on model forecasting accuracy from one to six hours ahead. These questions are investigated utilizing data from the FINO1 offshore platform and Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) C1 site, and testing results are compared to the persistence method. At FINO1, due to the presence of multiple wind farms and high inter-annual variability, RF is more effective as an error-correction mechanism for the persistence approach. The direct forecasting strategy is seen to slightly outperform the recursive strategy, specifically for forecasts three or more steps ahead. Finally, increased data availability (up to &sim;8 equivalent years of hourly training data) appears to continually improve forecasting accuracy, although changing environmental flow patterns have the potential to negate such improvement. We hope that the findings of this study will assist future researchers and industry professionals to construct accurate, reliable RF models for wind speed forecasting

Large-Scale Synoptic Systems and Fog During the C-FOG Field Experiment

AbstractThe goal of this work is to summarize synoptic meteorological conditions during the Coastal Fog (C-FOG) field project that took place onshore and offshore of the Avalon Peninsula, Newfoundland, from 25 August until 8 October 2018. Visibility was measured at three locations at the Ferryland supersite that are about 1&nbsp;km from each other, and at two additional sites 66 and 76&nbsp;km to the north. Supporting meteorological measurements included surface winds, air temperature, humidity, pressure, radiation, cloud-base height, and atmospheric thermodynamic profiles from radiosonde soundings. Statistics are presented for surface measurements during fog events including turbulence kinetic energy, net longwave radiation, visibility, and precipitation. Eleven fog events are observed at Ferryland. Each significant fog event is related to a large-scale cyclonic system. The longest fog event is due to interaction of a northern deep low and a tropical cyclone. Fog occurrence is also examined across Atlantic Canada by including Sable Island, Yarmouth, Halifax, and Sydney. It is concluded that at Ferryland, all significant fog events occur under a cyclonic system while at Sable Island all significant fog events occur under both cyclonic and anticyclonic systems. The fog-formation mechanism involves cloud lowering and stratus broadening or only stratus broadening for the cyclonic systems while for the anticyclonic systems it is stratus broadening or radiation. Although widely cited as the main cause of fog in Atlantic Canada, advection fog is not found to be the primary or sole fog type in the events examined

Marker-assisted identification of novel genetic lines for salinity tolerance and their categorization for utilization in development of hybrid rice (Oryza sativa L.)

Identification of new sources of salinity-tolerant genotypes is prerequisite for rice breeding programs in different saline ecosystems. In the present experiment, we characterized 177 landraces from the Western Ghats of Karnataka under natural saline field conditions for two years using morpho-physiological and grain quality parameters. Significant variation was present among landraces for seedling stage and reproductive stage salinity tolerance. The nutrient composition analysis of grain revealed an increase in average grain protein and carbohydrate content under saline conditions. Evaluation of twenty-two SSR markers associated with the Saltol region validated RM140, RM1287 and RM562 as best markers to classify landraces for saline tolerance. Polymorphism Information Content and genetic diversity indices showed that the markers RM10748 and RM10864 were highly useful for distinguishing landraces. Further, to benefit the exploitation of heterosis, eleven maintainers were identified among tolerant landraces and these genotypes could be further developed into male sterile lines for production of salinity-tolerant rice hybrids. Comparison with ‘Pokkali’ for morpho-physiological traits along with molecular confirmation showed that the landraces ‘Doddabaikalu,’ Kalaadikonda,’ Gajagunda’ and ‘Anekombina batha’ were superior donors carrying genomic regions for salinity tolerance