Estimation of monsoon wind characteristics in India

A detailed statistical analysis of monthly average wind speed data of monsoon period (June-September) for the year 1921-90 for 57 stations spread all over India have been reported. Probability densities, average wind speeds, standard deviations, kurtosis and skewness of wind speed frequency distribution for each station have been worked out. Histograms depicting relative frequency distribution of average wind speeds have also been prepared. It is observed that the different histograms do not exhibit any similarity among themselves indicating thereby that no single distribution is uniformly applicable for all the stations. It is also seen that the average wind speeds during monsoon period over major part of India varies from 7 to 14 kmph. Further, at most of the stations average monsoon wind speed is generally higher than average annual wind speeds. It is also noted that most of the time the wind speed exceeds 10 kmph in coastal regions of Gujarat and southern parts of the peninsular India. The information generated is of multi fold application such as (i) Identification of sites suitable for installation of Wind Energy Conversion Systems (ii) Development of Driving Rain Index and (iii) Design of buildings for creating comfortable environment indoors.

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SHAPING OF AN AUTONOMOUS POWER SYSTEM FOR GUARANTEED POWER SUPPLY WITH THE PREDOMINANCE WIND ENERGY

Alternative Energy and Ecology (ISJAEE) ◽

10.15518/isjaee.2018.22-24.028-050 ◽

2018 ◽

pp. 28-50

Author(s):

S. G. Ignatiev ◽

S. V. Kiseleva

Keyword(s):

Energy Storage ◽

Wind Speed ◽

Wind Energy ◽

Wind Turbine ◽

Wind Turbines ◽

Energy Demand ◽

Diesel Generator ◽

Average Wind Speed ◽

Wind Speeds ◽

Average Wind

Optimization of the autonomous wind-diesel plants composition and of their power for guaranteed energy supply, despite the long history of research, the diversity of approaches and methods, is an urgent problem. In this paper, a detailed analysis of the wind energy characteristics is proposed to shape an autonomous power system for a guaranteed power supply with predominance wind energy. The analysis was carried out on the basis of wind speed measurements in the south of the European part of Russia during 8 months at different heights with a discreteness of 10 minutes. As a result, we have obtained a sequence of average daily wind speeds and the sequences constructed by arbitrary variations in the distribution of average daily wind speeds in this interval. These sequences have been used to calculate energy balances in systems (wind turbines + diesel generator + consumer with constant and limited daily energy demand) and (wind turbines + diesel generator + consumer with constant and limited daily energy demand + energy storage). In order to maximize the use of wind energy, the wind turbine integrally for the period in question is assumed to produce the required amount of energy. For the generality of consideration, we have introduced the relative values of the required energy, relative energy produced by the wind turbine and the diesel generator and relative storage capacity by normalizing them to the swept area of the wind wheel. The paper shows the effect of the average wind speed over the period on the energy characteristics of the system (wind turbine + diesel generator + consumer). It was found that the wind turbine energy produced, wind turbine energy used by the consumer, fuel consumption, and fuel economy depend (close to cubic dependence) upon the specified average wind speed. It was found that, for the same system with a limited amount of required energy and high average wind speed over the period, the wind turbines with lower generator power and smaller wind wheel radius use wind energy more efficiently than the wind turbines with higher generator power and larger wind wheel radius at less average wind speed. For the system (wind turbine + diesel generator + energy storage + consumer) with increasing average speed for a given amount of energy required, which in general is covered by the energy production of wind turbines for the period, the maximum size capacity of the storage device decreases. With decreasing the energy storage capacity, the influence of the random nature of the change in wind speed decreases, and at some values of the relative capacity, it can be neglected.

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Analysis of Performance of the Wind-Driven Pulverizing Aerator Based on Average Wind Speeds in the Conditions of Góreckie Lake

Energies ◽

10.3390/en14102796 ◽

2021 ◽

Vol 14 (10) ◽

pp. 2796

Author(s):

Andrzej Osuch ◽

Ewa Osuch ◽

Stanisław Podsiadłowski ◽

Piotr Rybacki

Keyword(s):

Mathematical Model ◽

Wind Speed ◽

Water Pump ◽

Average Wind Speed ◽

Flow Rates ◽

Wind Speeds ◽

Average Wind ◽

Flow Through ◽

Analysis Of Performance ◽

Research Period

In the introduction to this paper, the characteristics of Góreckie lake and the construction and operation of the wind-driven pulverizing aerator are presented. The purpose of this manuscript is to determine the efficiency of the pulverizing aerator unit in the windy conditions of Góreckie Lake. The efficiency of the pulverization aerator depends on the wind conditions at the lake. It was necessary to conduct thorough research to determine the efficiency of water flow through the pulverization segment (water pump). It was necessary to determine the rotational speed of the paddle wheel, which depended on the average wind speed. Throughout the research period, measurements of hourly average wind speed were carried out. It was possible to determine the efficiency of the machine by developing a dedicated mathematical model. The latest method was used in the research, consisting of determining the theoretical volumetric flow rates of water in the pulverizing aerator unit, based on average hourly wind speeds. Pulverization efficiency under the conditions of Góreckie Lake was determined based on 6600 average wind speeds for spring, summer and autumn, 2018. Based on the model, the theoretical efficiency of the machine was calculated, which, under the conditions of Góreckie Lake, amounted to 75,000 m3 per year.

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Formation and distribution of sea-surface microlayers

Biogeosciences Discussions ◽

10.5194/bgd-7-5719-2010 ◽

2010 ◽

Vol 7 (4) ◽

pp. 5719-5755 ◽

Cited By ~ 4

Author(s):

O. Wurl ◽

E. Wurl ◽

L. Miller ◽

K. Johnson ◽

S. Vagle

Keyword(s):

Wind Speed ◽

Primary Production ◽

Sea Surface ◽

Surface Microlayer ◽

Average Wind Speed ◽

Wind Speeds ◽

Average Wind ◽

The Pacific ◽

Sea Surface Microlayer ◽

Natural Surfactants

Abstract. Results from a study of surfactants in the sea-surface microlayer (SML) in different regions of the ocean (subtropical, temperate, polar) suggest that this interfacial layer between the ocean and atmosphere covers the ocean's surface to a significant extent. Threshold values at which primary production acts as a significant source of natural surfactants have been derived from the enrichment of surfactants in the SML relative to underlying water and local primary production. Similarly, we have also derived a wind speed threshold at which the SML is disrupted. The results suggest that surfactant enrichment in the SML is typically greater in oligotrophic regions of the ocean than in more productive waters. Furthermore, the enrichment of surfactants persisted at wind speeds of up to 10 m s−1 without any observed depletion above 5 m s−1. This suggests that the SML is stable enough to exist even at the global average wind speed of 6.6 m s−1. Global maps of primary production and wind speed are used to estimate the ocean's SML coverage. The maps indicate that wide regions of the Pacific and Atlantic Oceans between 30° N and 30° S are more significantly affected by the SML than northern of 30° N and southern of 30° S due to higher productivity (spring/summer blooms) and wind speeds exceeding 12 m s−1 respectively.

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The super-turbine wind power conversion paradox: using machine learning to reduce errors caused by Jensen's inequality

Wind Energy Science ◽

10.5194/wes-4-343-2019 ◽

2019 ◽

Vol 4 (2) ◽

pp. 343-353 ◽

Cited By ~ 1

Author(s):

Tyler C. McCandless ◽

Sue Ellen Haupt

Keyword(s):

Machine Learning ◽

Standard Deviation ◽

Wind Speed ◽

Wind Power ◽

Power Conversion ◽

Jensen’S Inequality ◽

Jensen's Inequality ◽

Average Wind Speed ◽

Wind Speeds ◽

Average Wind

Abstract. Wind power is a variable generation resource and therefore requires accurate forecasts to enable integration into the electric grid. Generally, the wind speed is forecast for a wind plant and the forecasted wind speed is converted to power to provide an estimate of the expected generating capacity of the plant. The average wind speed forecast for the plant is a function of the underlying meteorological phenomena being predicted; however, the wind speed for each turbine at the farm is also a function of the local terrain and the array orientation. Conversion algorithms that assume an average wind speed for the plant, i.e., the super-turbine power conversion, assume that the effects of the local terrain and array orientation are insignificant in producing variability in the wind speeds across the turbines at the farm. Here, we quantify the differences in converting wind speed to power at the turbine level compared with a super-turbine power conversion for a hypothetical wind farm of 100 2 MW turbines as well as from empirical data. The simulations with simulated turbines show a maximum difference of approximately 3 % at 11 m s−1 with a 1 m s−1 standard deviation of wind speeds and 8 % at 11 m s−1 with a 2 m s−1 standard deviation of wind speeds as a consequence of Jensen's inequality. The empirical analysis shows similar results with mean differences between converted wind speed to power and measured power of approximately 68 kW per 2 MW turbine. However, using a random forest machine learning method to convert to power reduces the error in the wind speed to power conversion when given the predictors that quantify the differences due to Jensen's inequality. These significant differences can lead to wind power forecasters overestimating the wind generation when utilizing a super-turbine power conversion for high wind speeds, and indicate that power conversion is more accurately done at the turbine level if no other compensatory mechanism is used to account for Jensen's inequality.

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Field Study on the Microclimate of Public Spaces in Traditional Residential Areas in a Severe Cold Region of China

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16162986 ◽

2019 ◽

Vol 16 (16) ◽

pp. 2986 ◽

Cited By ~ 3

Author(s):

Yujie Lin ◽

Yumeng Jin ◽

Hong Jin

Keyword(s):

Wind Speed ◽

Temperature Difference ◽

Public Spaces ◽

Average Wind Speed ◽

Coverage Ratio ◽

Wind Speeds ◽

Average Temperature ◽

Average Temperature Difference ◽

Average Wind ◽

The Difference

As residential environment science advances, the environmental quality of outdoor microclimates has aroused increasing attention of scholars majoring in urban climate and built environments. Taking the microclimate of a traditional residential area in a severe cold city as the study object, this study explored the influence of spatial geometry factors on the microclimate of streets and courtyards by field measurements, then compared the differences in microclimate of distinct public spaces. The results are as follows. (1) The temperature of a NE-SW (Northeast-Southwest) oriented street was higher than that of a NW-SE (Northwest-Southeast) oriented street in both summer and winter, with an average temperature difference of 0.7–1.4 °C. The wind speeds in the latter street were slower, and the difference in average wind speed was 0.2 m/s. (2) In the street with a higher green coverage ratio, the temperature was much lower, a difference that was more obvious in summer. The difference in mean temperature was up to 1.2 °C. The difference in wind speed between the two streets was not obvious in winter, whereas the wind speed in summer was significantly lower for the street with a higher green coverage ratio, and the difference in average wind speed was 0.7 m/s. (3) The courtyards with higher SVF (sky view factor) had higher wind speeds in winter and summer, and the courtyards with larger SVF values had higher temperatures in summer, with an average temperature difference of 0.4 °C. (4) When the spaces had the same SVF values and green coverage ratios, the temperature of the street and courtyard were very similar, in both winter and summer. The wind speed of the street was significantly higher than the courtyard in summer, and the wind speed difference was 0.4 m/s.

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Monitoring Sand Drift Potential and Sand Dune Mobility over the Last Three Decades (Khartouran Erg, Sabzevar, NE Iran)

Sustainability ◽

10.3390/su13169050 ◽

2021 ◽

Vol 13 (16) ◽

pp. 9050

Author(s):

Mohammad Reza Rahdari ◽

Andrés Rodríguez-Seijo

Keyword(s):

Wind Speed ◽

Sand Dune ◽

Sand Dunes ◽

Average Wind Speed ◽

Wind Speeds ◽

Average Wind ◽

Sand Drift ◽

Drift Potential ◽

Long Time ◽

Ne Iran

Aeolian sediments cover about 6% of the earth’s surface, of which 97% occur in arid regions, and these sediments cover about 20% of the world’s lands. Sand drifts can harm sensitive ecosystems; therefore, this research has aimed to study wind regimes and the monitoring of sand drift potential and dune mobility in the Khartouran Erg (NE Iran). The study investigated 30 years of wind speed and direction to better understand sand dune mobility processes using the Fryberger and Tsoar methods. The results of the wind regime study showed that the eastern (33.4%) and northeastern (14.3%) directions were more frequent, but the study of winds greater than the threshold (6 m/s) in winter, spring, and autumn indicated the dominance of eastern and northern wind directions. Findings of calm winds showed that winters (40.4%) had the highest frequency, and summers (15%) had the lowest frequency; the annual frequency was 30%. The average wind speed in summers was the highest (4.38 m/s), and, in the winters, it was the lowest (2.28 m/s); the annual average wind speed was 3.3 m/s. The annual drift potential (DP = 173 VU) showed that it was categorized as low class, and the winds carried sand to the southwest. The monitoring of drift potential showed that there was a sharp increase between 2003 and 2008, which could have been attributed to a change in wind speeds in the region. Unite directional index, the index of directional variability, has been alternating from 0.3 to 0.6 for 30 years. Furthermore, monitoring of sand mobility recorded a value from 0.1 to 0.4, and the lowest and highest values were registered from 0.08 to 0.9, with an average of 0.27. Finally, it can be concluded that sand dunes have been fixed for a long time, and the intensity of the mobility index is affected by climate changes.

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The Super-Turbine Wind Power Conversion Paradox: Using Machine Learning to Reduce Errors Caused by Jensen’s Inequality

10.5194/wes-2018-74 ◽

2019 ◽

Author(s):

Tyler C. McCandless ◽

Sue Ellen Haupt

Keyword(s):

Machine Learning ◽

Standard Deviation ◽

Wind Speed ◽

Wind Power ◽

Power Conversion ◽

Jensen’S Inequality ◽

Jensen's Inequality ◽

Average Wind Speed ◽

Wind Speeds ◽

Average Wind

Abstract. Wind power is a variable generation resource and therefore requires accurate forecasts to enable integration into the electric grid. Generally, the wind speed is forecast for a wind plant and the forecasted wind speed is converted to power to provide an estimate of the expected generating capacity of the plant. The average wind speed forecast for the plant is a function of the underlying meteorological phenomena being predicted; however, the wind speed for each turbine at the farm is also a function of the local terrain and the array orientation. Conversion algorithms that assume an average wind speed for the plant, i.e., the super-turbine power conversion, assume that the effects of the local terrain and array orientation are insignificant in producing variability in the wind speeds across the turbines at the farm. Here, we quantify the differences in converting wind speed to power at the turbine level compared to a super-turbine power conversion for a hypothetical wind farm of 100 2-MW turbines as well as from empirical data. The simulations with simulated turbines show a maximum difference of approximately 3 % at 11 m s−1 with 1 m s−1 standard deviation of wind speeds and 8 % at 11 m s−1 with 2 m s−1 standard deviation of wind speeds as a consequence of Jensen’s Inequality. The empirical analysis shows similar results with mean differences between converted wind speed to power and measured power of approximately 68 kW per 2 MW turbine. However, using a random forest machine learning method to convert to power reduces the error in the wind speed to power conversion when given the predictors that quantify the differences due to Jensen’s Inequality. These significant differences can lead to wind power forecasters over-estimating the wind generation when utilizing a super-turbine power conversion for high wind speeds, and indicates that power conversion is more accurately done at the turbine level if no other compensatory mechanism is used to account for Jensen’s Inequality.

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ANALISA PERFORMA TURBIN ANGIN SUMBU HORISONTAL BERSUDU AIRFOIL MELALUI VARIASI JUMLAH SUDU

ROTOR ◽

10.19184/rotor.v11i2.9338 ◽

2018 ◽

Vol 11 (2) ◽

pp. 18

Author(s):

Wabang A Jhon ◽

Abanat D.J Jufra ◽

Hattu Edwin

Keyword(s):

Wind Speed ◽

Kinetic Energy ◽

Wind Turbine ◽

Wind Turbines ◽

Mechanical Energy ◽

Coefficient Of Performance ◽

Research Activity ◽

Average Wind Speed ◽

Wind Speeds ◽

Average Wind

Indonesia is an area that has the potential for sufficient wind resources to be utilized for kinetic energy into other energy such as mechanical energy and electrical energy through its generators (generators). The way to utilize wind kinetic energy into other energy is through a device called a wind turbine. Wind turbines have been around since ancient times, and are called airfoil angled wind turbines. This airfoil wind turbine is designed only for areas with average wind speeds above 6m / s. While in Indonesia not all regions have the same wind speed. In certain seasons, the average wind speed is below 6 m / s. This has become a major problem in regions that have average wind speeds below 6 m / s. Seeing this condition, there is a need for scientific research to obtain wind turbines that can be used in areas with average wind speeds below 5m / s. For this reason, the research I want to do is get a wind turbine that can be used as a power plant in areas that have wind speeds below 6m / s. This research was conducted on the basis of scientific theory in fluid mechanics regarding the sweeping area of wind turbines and the performance of variations in the number of blades in the wind. In addition, the research in several scientific journals was used as the basis of this research This research method is an experimental method, in the form of testing a wind turbine axis prototype horizontal and airfoil axis. The details of the research activity are the design and manufacture of laboratory scale horizontal airfoil axis turbines. Next, testing with a fan as a source of wind. The fan used has three variations of speed, all of which are used to determine the lowest average wind speed that can be applied. The results of the research are where wind turbines with the greatest torque and power and the Coefficient of Performance (CP) with the highest value will be used as a result to be applied to the community. Based on experimental data, it can be concluded that the greatest torque and power occur in turbines with 4 blades with details at speed 1, the largest torque and power are 0.201 Nm and 4.5 W; at speed 2, the biggest torque and power are 0.25 Nm and 7.21 W; at speed 3, the biggest torque and power are 0.28 Nm and 8.35 W Keywords: wind turbine, airfoil, nozzle, diffuser

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Continuous Recording of Areal Wind Speeds*

Bulletin of the American Meteorological Society ◽

10.1175/1520-0477-28.1.41 ◽

1947 ◽

Vol 28 (1) ◽

pp. 41-44 ◽

Cited By ~ 1

Author(s):

Ronald L. Ives

Keyword(s):

Wind Speed ◽

Continuous Recording ◽

Large Area ◽

Average Wind Speed ◽

Wind Speeds ◽

Average Wind

Methods of determining, and of recording continuously, the average wind speed over a large area, by means of a multiplicity of contacting anemometers electrically couple dto a modified Grinnell condenser-discharge recorder, are here outlined, with an empirical discussion of the attainable accuracy.

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Measuring Sub-second Wind Velocity Changes for Agricultural Drift One Meter Above the Ground

Applied Engineering in Agriculture ◽

10.13031/aea.12264 ◽

2019 ◽

Vol 35 (5) ◽

pp. 697-704

Author(s):

Matthew W. Schramm ◽

H Mark Hanna ◽

Matt J. Darr ◽

Steven J. Hoff ◽

Brian L. Steward

Keyword(s):

Wind Speed ◽

Solar Radiation ◽

Wind Velocity ◽

Wind Direction ◽

Weather Conditions ◽

Spray Drift ◽

Average Wind Speed ◽

Wind Speeds ◽

Average Wind ◽

Velocity Changes

Abstract. Agricultural spray drift is affected by many factors including current weather conditions, topography of the surrounding area, fluid properties at the nozzle, and the height at which the spray is released. During the late spring/summer spray seasons of 2014 and 2015, wind direction, speed, and solar radiation (2014 only) were measured at 10 Hz, 1 m above the ground to investigate conditions that are typically encountered by a droplet when released from a nozzle on an agricultural sprayer. Measurements of wind velocity as the wind passed from an upwind sensor to a downwind sensor were used to evaluate what conditions wind may be most likely to have a significant direction or speed change which affects droplet trajectory. For two individual datasets in which the average wind speed was 3.6 and 1.5 m/s (8.0 and 3.4 mi/h), there exists little linear correlation of wind speed or wind direction between an upwind and downwind anemometer separated by 30.5 m (100 ft). The highest observed correlation, resulting from a 12-s lag between the upwind and downwind datasets, was 0.29 when the average wind speed was 3.6 m/s (8.0 mi/h). Correlations greater than 0.1 were only found for wind speeds exceeding 3 m/s. Using this lag time, it was observed that the wind direction 30 s into the future had a 30% chance to be different by more than 20° from current conditions. A wind speed difference of more than 1 m/s (2.2 mi/h) from current conditions [mean wind speed was 3.6 m/s (8.0 mi/h)] was observed about 50% of the time. Analyzing 36 days of the 2014 and 2015 spray season wind velocity data showed that the most variability in wind direction occurred with wind speeds below 2 m/s (4.5 mi/h). Greater wind direction variability occurred in the mid-afternoon with higher solar radiation. Keywords: Sprayers, Spray drift, Spray droplets, Turbulence, Wind effects.

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