scholarly journals Standard Deviation of Wind Direction Estimated from Direct Observation of a Sensitive Wind Vane

1968 ◽  
Vol 7 (4) ◽  
pp. 714-715
Author(s):  
J. A. Turner
Keyword(s):  
Standard Deviation ◽  
Direct Observation ◽  
Wind Direction ◽  
Wind Vane

scholarly journals Improved Wind and Turbulence Measurements Using a Low-Cost 3-D Sonic Anemometer at a Low-Wind Site

2008 ◽  
Vol 2 (1) ◽  
pp. 131-138 ◽  
Author(s):  
Brent M. Bowen
Keyword(s):  
Standard Deviation ◽  
Wind Direction ◽  
Sonic Anemometer ◽  
Vertical Wind ◽  
Wind Speeds ◽  
Low Wind Speeds ◽  
Mechanical Sensors ◽  
Wind Vane ◽  
Cup Anemometer ◽  
Propeller Anemometer

A year of data from sonic anemometer and mechanical wind sensors was analyzed and compared at a low-wind site. Results indicate that 15-minute average and peak 1-second wind speeds (u) from the sonic agree well with data derived from a co-located cup anemometer over a wide range of speeds. Wind direction data derived from the sonic also agree closely with those from a wind vane except for very low wind speeds. Values of standard deviation of longitudinal wind speed (σu) and wind direction fluctuations (σø) from the sonic and mechanical sensors agree well for times with u > 2 ms-1 but show significant differences with lower u values. The most significant differences are associated with the standard deviation of vertical wind fluctuations (σw): the co-located vertical propeller anemometer yields values increasingly less than those measured by the sonic anemometer as u decreases from 2.5 approaching 0 ms-1. The combination of u over-estimation and under-estimation of σw from the mechanical sensors at low wind speeds causes considerable underestimation of the standard deviation of vertical wind angle fluctuations (σø), an indicator of vertical dispersion. Calculations of σø from sonic anemometer measurements are typically 5° to 10° greater than from the mechanical sensors when the mechanical instruments indicate that σø < 5° or so. The errors with the propeller anemometer, cup anemometer and wind vane, caused by their inability to respond to higher frequency (smaller scale) turbulent fluctuations, can therefore lead to large (factors of 2 to 10 or more) errors in both the vertical and horizontal dispersion during stable conditions with light winds. The sonic anemometer clearly provides more accurate and reliable wind data than the mechanical wind sensor with u < 2.5 ms-1

Design of Small Wind Turbine

2008 ◽  
Author(s):  
R. S. Amano ◽  
Ryan Malloy
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Direction ◽  
Pitch Angle ◽  
Bridge Circuit ◽  
Small Wind Turbine ◽  
Swash Plate ◽  
Wind Vane ◽  
Blade Pitch Angle

The project has been completed, and all of the aforementioned objectives have been achieved. An anemometer has been constructed to measure wind speed, and a wind vane has been built to sense wind direction. An LCD module has been acquired and has been programmed to display the wind speed and its direction. An H-Bridge circuit was used to drive a gear motor that rotated the nacelle toward the windward direction. Finally, the blade pitch angle was controlled by a swash plate mechanism and servo motors installed on the generator itself. A microcontroller has been programmed to optimally control the servo motors and gear motor based on input from the wind vane and anemometer sensors.

scholarly journals On the Algorithms Used to Compute the Standard Deviation of Wind Direction

2009 ◽  
Vol 48 (10) ◽  
pp. 2144-2151 ◽  
Author(s):  
Pierre S. Farrugia ◽  
James L. Borg ◽  
Alfred Micallef
Keyword(s):  
Standard Deviation ◽  
Wind Direction ◽  
Atmospheric Stability ◽  
Angular Distance ◽  
Circular Statistics ◽  
Horizontal Wind ◽  
Stability Class ◽  
Mathematical Expressions ◽  
Analytical Work

Abstract The standard deviation of wind direction is a very important quantity in meteorology because in addition to being used to determine the dry deposition rate and the atmospheric stability class, it is also employed in the determination of the rate of horizontal diffusion, which in turn determines transport and dispersion of air pollutants. However, the computation of this quantity is rendered difficult by the fact that the horizontal wind direction is a circular variable having a discontinuity at 2π radians, beyond which the wind direction starts again from zero, thus preventing angular subtraction from being a straightforward procedure. In view of such a limitation, this work is meant to provide new mathematical expressions that simplify both the computational and analytical work involved in handling the standard deviation of wind direction. This is achieved by deriving a number of Fourier series and Taylor expansions that can represent the minimum angular distance and its powers. Using these expressions, the relation between two algorithms commonly used to determine the standard deviation of wind direction is analyzed. Furthermore, given that these trigonometric expansions effectively reduce the mathematical complexity involved when dealing with circular statistics, their potential application to solve other problems is discussed.

scholarly journals Determination of optimal wind turbine alignment into the wind and detection of alignment changes with SCADA data

2018 ◽  
Vol 3 (1) ◽  
pp. 395-408 ◽  
Author(s):  
Niko Mittelmeier ◽  
Martin Kühn
Keyword(s):  
Wind Direction ◽  
Energy Yield ◽  
Measurement Equipment ◽  
Power Losses ◽  
Multilinear Regression ◽  
Flow Distortion ◽  
Operational Lifetime ◽  
Flow Equilibrium ◽  
Ground Based Lidar ◽  
Wind Vane

Abstract. Upwind horizontal axis wind turbines need to be aligned with the main wind direction to maximize energy yield. Attempts have been made to improve the yaw alignment with advanced measurement equipment but most of these techniques introduce additional costs and rely on alignment tolerances with the rotor axis or the true north. Turbines that are well aligned after commissioning may suffer an alignment degradation during their operational lifetime. Such changes need to be detected as soon as possible to minimize power losses. The objective of this paper is to propose a three-step methodology to improve turbine alignment and detect changes during operational lifetime with standard nacelle metrology (met) mast instruments (here: two cup anemometer and one wind vane). In step one, a reference turbine and an external undisturbed reference wind signal, e.g., met mast or lidar are used to determine flow corrections for the nacelle wind direction instruments to obtain a turbine alignment with optimal power production. Secondly a nacelle wind speed correction enables the application of the previous step without additional external measurement equipment. Step three is a monitoring application and allows the detection of alignment changes on the wind direction measurement device by means of a flow equilibrium between the two anemometers behind the rotor. The three steps are demonstrated at two 2 MW turbines together with a ground-based lidar. A first-order multilinear regression model gives sufficient correction of the flow distortion behind the rotor for our purposes and two wind vane alignment changes are detected with an accuracy of ±1.4∘ within 3 days of operation after the change is introduced. We show that standard turbine equipment is able to align a turbine with sufficient accuracy and changes to its alignment can be detected in a reasonably short time, which helps to minimize power losses.

scholarly journals Wind direction estimation using SCADA data with consensus-based optimization

2019 ◽  
Vol 4 (2) ◽  
pp. 355-368 ◽  
Author(s):  
Jennifer Annoni ◽  
Christopher Bay ◽  
Kathryn Johnson ◽  
Emiliano Dall'Anese ◽  
Eliot Quon ◽  
...  
Keyword(s):  
Real Time ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Control Strategies ◽  
Reliable Estimate ◽  
Local Conditions ◽  
Efficient Operation ◽  
Wind Vane ◽  
Inflow Conditions

Abstract. Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. To enable cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving a more efficient wind farm is to develop algorithms that ensure reliable, robust, real-time, and efficient operation of wind turbines in a wind farm using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for developing a cooperative wind farm that incorporates information from nearby turbines in real time to better align turbines in a wind farm. SCADA data from multiple turbines can be used to make better estimates of the local inflow conditions at each individual turbine. By incorporating measurements from multiple nearby turbines, a more reliable estimate of the wind direction can be obtained at an individual turbine. The consensus-based approach presented in this paper uses information from nearby turbines to estimate wind direction in an iterative way rather than aggregating all the data in a wind farm at once. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction. This estimated wind direction signal has implications for potentially decreasing dynamic yaw misalignment, decreasing the amount of time a turbine spends yawing due to a more reliable input to the yaw controller, increasing resiliency to faulty wind-vane measurements, and increasing the potential for wind farm control strategies such as wake steering.

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