Quantitative Assessment of Human Wind Speed Overestimation

AbstractHuman wind reports are a vital supplement to the relatively sparse network of automated weather stations in the United States, especially for localized convective winds. In this study, human wind estimates recorded in Storm Data between 1996 and 2013 were compared with instrumentally observed wind speeds from the Global Historical Climatology Network (GHCN). Nonconvective wind events in areas of flat terrain within the continental United States served as the basis for this analysis because of the relative spatial homogeneity of wind fields in these meteorological and geographic settings. The distribution of 6801 GHCN-measured gust factors (GF), defined here as the ratio of the daily maximum gust to the daily average wind, provided the reference upon which human gust reports were judged. GFs were also calculated for each human estimate by dividing the estimated gust by the GHCN average wind speed on that day. Human-reported GFs were disproportionately located in the upper tail of the observed GF distribution, suggesting that humans demonstrate a tendency to report statistically improbable wind gusts. As a general rule of thumb, humans overestimated nonconvective wind GFs by approximately one-third.

Download Full-text

Meteorologically Stratified Gust Factors for Forecasting Peak Wind Gusts Across the United States

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-21-0013.1 ◽

2021 ◽

pp. 1-17

Author(s):

Jonathan D. W. Kahl ◽

Brandon R. Selbig ◽

Austin R. Harris

Keyword(s):

United States ◽

Wind Speed ◽

Power Distribution ◽

The United States ◽

Wind Gust ◽

Average Wind Speed ◽

Gust Factor ◽

Wind Gusts ◽

Wide Range ◽

Gust Factors

AbstractWind gusts are common to everyday life and affect a wide range of interests including wind energy, structural design, forestry, and fire danger. Strong gusts are a common environmental hazard that can damage buildings, bridges, aircraft, and trains, and interrupt electric power distribution, air traffic, waterways transport, and port operations. Despite representing the component of wind most likely to be associated with serious and costly hazards, reliable forecasts of peak wind gusts have remained elusive. A project at the University of Wisconsin-Milwaukee is addressing the need for improved peak gust forecasts with the development of the meteorologically stratified gust factor (MSGF) model. The MSGF model combines gust factors (the ratio of peak wind gust to average wind speed) with wind speed and direction forecasts to predict hourly peak wind gusts. The MSGF method thus represents a simple, viable option for the operational prediction of peak wind gusts. Here we describe the results of a project designed to provide the site-specific gust factors necessary for operational use of the MSGF model at a large number of locations across the United States. Gust web diagrams depicting the wind speed- and wind direction-stratified gust factors, as well as peak gust climatologies, are presented for all sites analyzed.

Download Full-text

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.

Download Full-text

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.

Download Full-text

Some statistical characteristics of monthly average wind speed at various heights

Renewable and Sustainable Energy Reviews ◽

10.1016/j.rser.2007.01.014 ◽

2008 ◽

Vol 12 (6) ◽

pp. 1712-1724 ◽

Cited By ~ 64

Author(s):

Károly Tar

Keyword(s):

Wind Speed ◽

Statistical Characteristics ◽

Average Wind Speed ◽

Monthly Average ◽

Average Wind

Download Full-text

Representing Extremes in a Daily Gridded Precipitation Analysis over the United States: Impacts of Station Density, Resolution, and Gridding Methods

Journal of Climate ◽

10.1175/jcli-d-13-00319.1 ◽

2014 ◽

Vol 27 (14) ◽

pp. 5201-5218 ◽

Cited By ~ 47

Author(s):

Melissa Gervais ◽

L. Bruno Tremblay ◽

John R. Gyakum ◽

Eyad Atallah

Keyword(s):

United States ◽

Extreme Precipitation ◽

The United States ◽

Low Resolution ◽

Spatial Homogeneity ◽

Precipitation Analysis ◽

Precipitation Station ◽

Density Resolution ◽

Analysis Point ◽

Station Density

Abstract This study focuses on errors in extreme precipitation in gridded station products incurred during the upscaling of station measurements to a grid, referred to as representativeness errors. Gridded precipitation station analyses are valuable observational data sources with a wide variety of applications, including model validation. The representativeness errors associated with two gridding methods are presented, consistent with either a point or areal average interpretation of model output, and it is shown that they differ significantly (up to 30%). An experiment is conducted to determine the errors associated with station density, through repeated gridding of station data within the United States using subsequently fewer stations. Two distinct error responses to reduced station density are found, which are attributed to differences in the spatial homogeneity of precipitation distributions. The error responses characterize the eastern and western United States, which are respectively more and less homogeneous. As the station density decreases, the influence of stations farther from the analysis point increases, and therefore, if the distributions are inhomogeneous in space, the analysis point is influenced by stations with very different precipitation distributions. Finally, ranges of potential percent representativeness errors of the median and extreme precipitation across the United States are created for high-resolution (0.25°) and low-resolution areal averaged (0.9° lat × 1.25° lon) precipitation fields. For example, the range of the representativeness errors is estimated, for annual extreme precipitation, to be from +16% to −12% in the low-resolution data, when station density is 5 stations per 0.9° lat × 1.25° lon grid box.

Download Full-text

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.

Download Full-text

Remaining Useful Life Estimation of Wind Turbine Blades under Variable Wind Speed Conditions Using Particle Filters

Annual Conference of the PHM Society ◽

10.36001/phmconf.2018.v10i1.481 ◽

2018 ◽

Vol 10 (1) ◽

Author(s):

Bhavana Valeti ◽

Shamim N. Pakzad

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Turbine Blades ◽

Remaining Useful Life ◽

Structural Components ◽

Wind Turbine Blades ◽

Average Wind Speed ◽

Average Wind ◽

Useful Life ◽

Variable Wind

Rotor blades are the most complex structural components in a wind turbine and are subjected to continuous cyclic loads of wind and self-weight variation. The structural maintenance operations in wind farms are moving towards condition based maintenance (CBM) to avoid premature failures. For this, damage prognosis with remaining useful life (RUL) estimation in wind turbine blades is necessary. Wind speed variation plays an important role influencing the loading and consequently the RUL of the structural components. This study investigates the effect of variable wind speed between the cutin and cut-out speeds of a typical wind farm on the RUL of a damage detected wind turbine blade as opposed to average wind speed assumption. RUL of wind turbine blades are estimated for different initial crack sizes using particle filtering method which forecasts the evolution of fatigue crack addressing the non-linearity and uncertainty in crack propagation. The stresses on a numerically simulated life size onshore wind turbine blade subjected to the above wind speed loading cases are used in computing the crack propagation observation data for particle filters. The effects of variable wind speed on the damage propagation rates and RUL in comparison to those at an average wind speed condition are studied and discussed.

Download Full-text

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.

Download Full-text

Relations between Temperature and Residential Natural Gas Consumption in the Central and Eastern United States

Journal of Applied Meteorology and Climatology ◽

10.1175/2007jamc1552.1 ◽

2007 ◽

Vol 46 (11) ◽

pp. 1993-2013 ◽

Cited By ~ 17

Author(s):

Reed P. Timmer ◽

Peter J. Lamb

Keyword(s):

United States ◽

Natural Gas ◽

Time Scales ◽

The United States ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

The South ◽

Northern Regions ◽

Gas Consumption

Abstract The increased U.S. natural gas price volatility since the mid-to-late-1980s deregulation generally is attributed to the deregulated market being more sensitive to temperature-related residential demand. This study therefore quantifies relations between winter (November–February; December–February) temperature and residential gas consumption for the United States east of the Rocky Mountains for 1989–2000, by region and on monthly and seasonal time scales. State-level monthly gas consumption data are aggregated for nine multistate subregions of three Petroleum Administration for Defense Districts of the U.S. Department of Energy. Two temperature indices [days below percentile (DBP) and heating degree-days (HDD)] are developed using the Richman–Lamb fine-resolution (∼1° latitude–longitude) set of daily maximum and minimum temperatures for 1949–2000. Temperature parameters/values that maximize DBP/HDD correlations with gas consumption are identified. Maximum DBP and HDD correlations with gas consumption consistently are largest in the Great Lakes–Ohio Valley region on both monthly (from +0.89 to +0.91) and seasonal (from +0.93 to +0.97) time scales, for which they are based on daily maximum temperature. Such correlations are markedly lower on both time scales (from +0.62 to +0.80) in New England, where gas is less important than heating oil, and on the monthly scale (from +0.55 to +0.75) across the South because of low January correlations. For the South, maximum correlations are for daily DBP and HDD indices based on mean or minimum temperature. The percentiles having the highest DBP index correlations with gas consumption are slightly higher for northern regions than across the South. This is because lower (higher) relative (absolute) temperature thresholds are reached in warmer regions before home heating occurs. However, these optimum percentiles for all regions are bordered broadly by surrounding percentiles for which the correlations are almost as high as the maximum. This consistency establishes the robustness of the temperature–gas consumption relations obtained. The reference temperatures giving the highest HDD correlations with gas consumption are lower for the colder northern regions than farther south where the temperature range is truncated. However, all HDD reference temperatures greater than +10°C (+15°C) yield similar such correlations for northern (southern) regions, further confirming the robustness of the findings. This robustness, coupled with the very high correlation magnitudes obtained, suggests that potentially strong gas consumption predictability would follow from accurate seasonal temperature forecasts.

Download Full-text