Wind Direction Measurements Research Articles

Using K-Means Clustering in Python with Periodic Boundary Conditions

Periodic boundary conditions are natural in many scientific problems, and often lead to particular symmetries. Working with datasets that express periodicity properties requires special approaches when analyzing these phenomena. Periodic boundary conditions often help to solve or describe the problem in a much simpler way. The angular rotational symmetry is an example of periodic boundary conditions. This symmetry implies angular momentum conservation. On the other hand, clustering is one of the first and most basic methods used in data analysis. It is often a starting point when new data are acquired and understood. K-means clustering is one of the most commonly used clustering methods. It can be applied to many different situations with reasonably good results. Unfortunately, the original k-means approach does not cope well with the periodic properties of the data. For example, the original k-means algorithm treats a zero angle as very far from an angle that is 359 degrees. Periodic boundary conditions often change the classical distance measure and introduce an error in k-means clustering. In the paper, we discuss the problem of periodicity in the dataset and present a periodic k-means algorithm that modifies the original approach. Considering that many data scientists prefer on-the-shelf solutions, such as libraries available in Python, we present how easily they can incorporate periodicity into existing k-means implementation in the PyClustering library. It allows anyone to integrate periodic conditions without significant additional costs. The paper evaluates the described method using three different datasets: the artificial dataset, wind direction measurement, and the New York taxi service dataset. The proposed periodic k-means provides better results when the dataset manifests some periodic properties.

Vehicle-Deployed Off-Axis Integrated Cavity Output Spectroscopic CH4/C2H6 Sensor System for Mobile Inspection of Natural Gas Leakage.

A vehicle-deployed parts-per-billion in volume (ppbv)-level off-axis integrated cavity output spectroscopic (OA-ICOS) CH4/C2H6 sensor system was experimentally presented for mobile inspection of natural gas leakage in urban areas. For the time-division-multiplexing-based dual-gas sensor system, an antivibration 35-cm-long optical cavity with an effective path length of ∼2510 m was fabricated with a high-stability temperature and pressure control design. An Allan deviation analysis yielded a minimum detection limit of 0.2 ppbv for CH4 detection and 10 ppbv for C2H6 detection for a 1 s averaging time. A natural gas leakage source location algorithm was proposed using an improved hybrid Nelder-Mead simplex search method and a particle swarm optimization (NM-PSO) algorithm. For field industrial application, the accuracy of the sensor system and leakage source location algorithm was confirmed through a CH4/C2H6 cylinder leakage experiment on the campus. Furthermore, through natural gas pipeline network inspection measurements in urban areas, three types of leakage sources, including natural gas, biogas, and possible leakage source were respectively located and confirmed using the global positioning system and wind speed and direction measurement system, verifying the reliability and potential application of the vehicle-deployed inspection system for future natural gas pipeline leakage monitoring.

Lifetime fatigue response due to wake steering on a pair of utility-scale wind turbines

Quantifying the impacts on turbine loads during wind farm control is an important consideration in assessing power production benefits. Wake steering controls aim to improve total wind farm performance by coordinating the control actions of individual turbines, wherein an upstream turbine is intentionally yawed at an offset angle from the measured wind direction. Consequently, this redirects its wake for improved power production and potentially reduces fatigue loads of the downstream turbines. This paper studies the lifetime fatigue loads associated with wake steering by using utility-scale wind turbine experimental data to conduct an analysis on a pair of wind turbines. This study was part of a large field experiment in which a group of five GE 1.5-MW SLE CWE turbines were selected as targets for conducting wake steering research. A standard loads instrumentation package and data acquisition system were installed on two turbines within the cluster to measure turbine fundamental loads. The time-series databases were used to calculate loads statistics as well as short-term and lifetime damage equivalent loads. Fatigue calculations followed the guidance in Annex H of the International Electrotechnical Commission standard 61400-1, Edition 4. Lifetime fatigue calculation results are presented in this analysis; three methods of assessing lifetime fatigue were used to determine percent difference for blade root moments, main shaft moments, main shaft torque, and tower top torque. For all three fatigue treatments, some components’ lifetime fatigue increases for the controlled turbine; however, the downwind turbine experienced a reduction in lifetime fatigue and combined effect for the turbine pair results in a reduction of fatigue when wake steering controls are applied.

Progress towards an HF Radar Wind Speed Measurement Method Using Machine Learning

HF radars are now an important part of operational coastal observing systems where they are used primarily for measuring surface currents. Their use for wave and wind direction measurement has also been demonstrated. These measurements are based on physical models of radar backscatter from the ocean surface described in terms of its ocean wave directional spectrum and the influence thereon of the surface current. Although this spectrum contains information about the local wind that is generating the wind sea part of the spectrum, it also includes spectral components propagating into the local area having been generated by winds away from the area i.e., swell. In addition, the relationship between the local wind sea and wind speed depends on fetch and duration. Thus, finding a physical model to extract wind speed from the radar signal is not straightforward. In this paper, methods that have been proposed to date will be briefly reviewed and an alternative approach is developed using machine learning methods. These have been applied to three different data sets using different radar systems in different locations. The results presented here are encouraging and proposals for further development are outlined.

Evaluating the use of an Unmanned Aerial Vehicle (UAV)-based active AirCore system to quantify methane emissions from dairy cows

Enteric fermentation and manure methane emissions from livestock are major anthropogenic greenhouse gas emissions. In general, direct measurements of farm-scale methane emissions are scarce due to the source complexity and the limitations of existing atmospheric sampling methods. Using an innovative UAV-based active AirCore system, we have performed accurate atmospheric measurements of CH4 mole fractions downwind of a dairy cow farm in the Netherlands on four individual days during the period from March 2017 to March 2019. The total CH4 emission rates from the farm were determined using the UAV-based mass balance approach to be 1.1–2.4 g/s. After subtracting estimated emission factors of manure onsite, we derived the enteric emission factors to be 0.20–0.51 kgCH4/AU/d (1 AU = 500 kg animal weight) of dairy cows. We show that the uncertainties of the estimates were dominated by the variabilities in the wind speed and the angle between the wind and the flight transect. Furthermore, nonsimultaneous sampling in the vertical direction of the plume is one of the main limiting factors to achieving accurate estimate of the CH4 emissions from the farm. In addition, a N2O tracer release experiment at the farm was performed when both a UAV and a mobile van were present to simultaneously sample the N2O tracer and the CH4 plumes from the farm, improving the source quantification with a correction factor of 1.04 and 1.22 for the inverse Gaussian approach and for the mass balance approach, respectively. The UAV-based active AirCore system is capable of providing useful estimates of CH4 emissions from dairy cow farms. The uncertainties of the estimates can be improved when combined with accurate measurements of local wind speed and direction or when combined with a tracer approach.

Analytic Model of Dual-Layer-Structure MEMS Thermal Wind Sensor With Increased Sensitivity

An analytic model of dual-layer-structure (DLS) microelectromechanical system (MEMS) thermal wind sensor was proposed in this article. Heat conduction and heat convective theory were applied to obtain the temperature difference between upstream and downstream under different wind speeds and directions. Simulation by finite element method (FEM) was performed to verify the proposed model. The DLS MEMS wind sensor was fabricated by commercial foundry with two polysilicon layers and one metal layer. After being packaged, the wind sensor was tested based on front surface sensing mode in the wind tunnel up to 33 m/s. The results demonstrated that the wind speed measurement agrees with the proposed model well and the average error of output voltage was no more than 8 mV. Moreover, the measured wind direction increased with the actual value marked on the base from 0° to 360°, which was consistent with the established model. Moreover, the average error was close to 3.7° and the maximum value was no more than 6.38°, which may be caused by asymmetry in scribing and packaging.

Coriolis effect recorded in Late Pleistocene Marine Isotope Stage 5e Bahamian aeolianites

Abstract The windward islands of the Lucayan Archipelago (Bahamas) form an Atlantic Ocean–facing transect spanning >950 km in length and 6° of latitude. The islands' topography is largely constructed from carbonate wind-blown dunes (i.e., aeolianites) deposited during the interglacial phases of the Late Pleistocene and Holocene. New digital elevation data from satellite radar interferometry (TanDEM-X German Earth observation satellite) enables a step change in the ability to map and quantify Bahamian aeolian landforms across the archipelago. A semi-automated mapping approach that leverages object-based image analysis yields a total aeolianite area of ~1674 km2 across Great Abaco, Eleuthera, Cat, San Salvador, Long, Crooked, Acklins, and Mayaguana islands (Bahamas) and the Turks and Caicos Islands. Longitudinal axis measurements from 747 Pleistocene parabolic dunes record increasing consistency of east-west orientation with decreasing latitude. Three U.S. National Data Buoy Center data buoys provided modern wind direction and velocity measurements (n = 730,933 of each) along this transect. Analysis of wind vectors (>P90 [90th percentile], n = 70,095) demonstrates increasing organization of easterlies at southern latitudes and an offset in directionality compared to formational winds of Pleistocene Marine Isotope Stage (MIS) 5e deposits. Southward trends of increasing wind strength and consistency reflect geostrophic flow driven by atmospheric circulation within the Hadley cell and right-hand deflection of the Coriolis effect in the Northern Hemisphere. We propose that the offset in directionality between dune axes and modern wind vectors is related to changes in latitudinal width of the Hadley cell from the Late Pleistocene (MIS 5e) to today. This data set is robust enough to serve as a benchmark against which future atmospheric circulation models can be compared.

The Anatomy of the South Carolina U.S.A. Coastal Sea Breeze 2019-2020

The study presented herein is the analysis of Sodar based instrument measurements of air temperature, dew point, and vertical wind speed and direction, recorded at two South Carolina sites, Waties Island in North Myrtle Beach and in Sumter, and at three atmospheric ground stations. Two of the ground stations are National Weather Service stations near the Sodars and one is a Coastal Carolina University Sea-Econet, as a part of the National Oceanic & Atmospheric Administration, MESO program, weather-sensor site on the Coastal Carolina University campus in Conway South Carolina. Objectives of this study are to establish specific values of winds, land and sea temperatures, precipitation and dew points associated with the changes induced by passages of the Sea Breeze Front, and to examine differences in the station-to-station incarnation of the Sea Breeze circulation. Variability from station to station in the nature and timing of Sea Breeze Front passage is found to be a function of relative proximity to the coast with Sea Breeze Front passage occurring earliest at the North Myrtle Beach site (the station at the coast), then at Sumter (~100 km inland) and finally Aiken at >100 km inland. Satellite based estimates of the percentages of onshore penetration distances from the coast are depicted. Wind vectors and air temperatures associated with onsets and passages of the Sea Breeze display robust wind fields directed onshore perpendicular to the coastline. Kinematical descriptors of the Sea Breeze wind particle motions are presented and display coherent stable elliptical motions during the late summer to early fall but are absent during the winter.

Design and Optimization of Resonant Cavity Piezoelectric Cantilever Sensor for Wind Velocity and Direction Measurement

Design and Optimization of Resonant Cavity Piezoelectric Cantilever Sensor for Wind Velocity and Direction Measurement

Mechanical Wind Sensor Based on Additive Manufacturing Technology

In this paper, a mechanical wind sensor based on additive manufacturing technology is demonstrated for the first time. The sensor is composed of a cylindrical sensing structure vertically placed in the center of a cross-shape cantilever. Four strain gauges are attached to the surface of the beams to monitor the wind-induced cantilever deformation which can reveal the speed and direction of airflow. Unlike traditional mechanical sensors, the proposed wind sensor is fabricated by additive manufacturing. With this new manufacturing method, the wind sensor can not only be flexibly designed to avoid mechanical wear, but also utilize corrosion-resistant organic materials for improved reliability. The designed wind sensor is implemented using a commercially-available three-dimensional (3D) printer, and installed in a wind tunnel for the experiment. The test results show that the proposed 3D printed mechanical wind sensor can achieve a measurement speed range up to 30 m/s, with a maximum error of 0.87 m/s, while the maximum errors for wind direction measurement are ±8°, over the full range of 360°. The proposal of this 3D printed wind sensor provides a new paradigm for developing small-size, low-cost, and high-reliability wind sensors.

Compact spherical-shaped airflow sensor for measuring wind direction and velocity during drones flying

Compact spherical-shaped airflow sensor for measuring wind direction and velocity during drones flying

Experimental results of wake steering using fixed angles

Abstract. In this article, the authors present a test of wake steering at a commercial wind farm. A single fixed yaw offset, rather than an optimized offset schedule, is alternately applied to an upstream wind turbine, and the effect on downstream turbines is analyzed. This experimental design allows for comparison with engineering wake models independent of the controller's ability to track a varying offset and correctly measure wind direction. Additionally, by applying the same offset in beneficial and detrimental conditions, we are able to collect important data for assessing second-order wake model predictions. Results of the article from collected data show good agreement with the FLOw Redirection and Induction in Steady State (FLORIS) engineering model and offer support for the asymmetry of wake steering predicted by newer models, such as the Gauss–curl hybrid model.

Quantifying methane emissions from coal mining ventilation shafts using an unmanned aerial vehicle (UAV)-based active AirCore system

A large quantity of CH4 is emitted to the atmosphere via ventilation shafts of underground coal mines. According to the European Pollutant Release and Transfer Register (E-PRTR), hard coal mines in the Upper Silesia Coal Basin (USCB) are a strong contributor (447 kt CH4 in 2017) to the annual European CH4 emissions. However, atmospheric emissions of CH4 from coal mines are poorly characterized, as they are dispersed over large areas. As part of the Carbon Dioxide and CH4 Mission (CoMet) pre-campaign, a study of the USCB's regional CH4 emissions took place in August 2017. We flew a recently developed active AirCore system aboard an unmanned aerial vehicle (UAV) to obtain CH4 mole fractions downwind of a single coal mining ventilation shaft. Besides CH4, we also measured CO2, CO, atmospheric temperature, pressure, and relative humidity. Wind-speed and wind-direction measurements were made using a lightweight balloon-borne radiosonde. Fifteen UAV flights were performed flying perpendicular to the wind direction at several altitude levels, to effectively build a ‘curtain’ of CH4 mole fractions in a two-dimensional plane at a distance between 150 and 350 m downwind of a single ventilation shaft. Furthermore, we have developed an inverse Gaussian approach for quantifying CH4 emissions from a point source using the UAV-based observations, and have applied it as well as the mass balance approach to both simulated data and actual flight data to quantify CH4 emissions. The simulated data experiments revealed the importance of having multiple transects at different altitudes, appropriate vertical spacing between the individual transects, and proper distance between the center height of the plume and the center flight transect. They also showed that the inverse Gaussian approach performed better than the mass balance approach. Our estimate of the CH4 emission rates from the sampled shaft ranges from 0.5 to 14.5 kt/year using a mass balance approach, and between 1.1 and 9.0 kt/year using an inverse Gaussian method. The average difference between the mass balance and the inverse Gaussian approach was 2.3 kt/year. Based on the observed correlation between CO2 and CH4 (R-squared > 0.69), the CO2 emissions from the shaft were estimated to be between 0.3 and 9.8 kt/year. This study demonstrates that the UAV-based active AirCore system provides an effective way of quantifying coal mining shaft emissions of CH4 and CO2.

A Digital-Simulation Model for a Full-Polarized Microwave Radiometer System and Its Calibration

A digital-correlation full-polarized microwave radiometer is an important passive remote sensor, as it can obtain the amplitude and phase information of an electromagnetic wave at the same time. It is widely used in the measurement of sea surface wind speed and direction. Its configuration is complicated, so the error analysis of the instrument is often difficult. This paper presents a full-polarized radiometer system model that can be used to analyze various errors, which include input signal models and a full-polarized radiometer (receiver) model. The input signal models are generated by WGN (white Gaussian noise), and the full-polarized radiometer model consists of an RF front-end model and digital back-end model. The calibration matrix is obtained by solving the overdetermined equations, and the output voltage is converted into Stokes brightness temperature through the calibration matrix. Then, we use the four Stokes parameters to analyze the sensitivity, linearity, and calibration residuals, from which the simulation model is validated. Finally, two examples of error analysis, including gain imbalance and quantization error, are given through a simulation model. In general, the simulation model proposed in this paper has good accuracy and can play an important role in the error analysis and pre-development of the fully polarized radiometer.

Rancang Bangun Sensor Node untuk Monitoring Parameter Cuaca dan PM2.5 Menggunakan Arduino WiFi

Sensor node untuk monitoring parameter cuaca dan PM2.5 terdiri dari sensor parameter cuaca berupa suhu, kelembaban, intensitas cahaya, curah hujan, kecepatan angin dan arah angin. Selain itu terdapat parameter kualitas udara yaitu sensor PM2.5. Pengolahan dan pengiriman data dilakukan oleh Arduino Uno WiFi yang merupakan perangkat mikrokontroller yang sudah terintegrasi dengan ESP8226 yang merupakan modul untuk mengkoneksikan dengan jaringan WiFi. Data sensor dikirimkan dari Arduino ke data base server untuk selanjutnya ditampilkan pada website. Hasil pengujian masing-masing sensor dibandingkan data BMKG menunjukkan hasil kesalahan pengukuran yang kecil untuk parameter suhu dan curah hujan dengan masing-masing prosentase kesalahan sebesar 4.61% dan 9.95%. Hasil pengukuran parameter kecepatan angin memiliki kesalahan cukup besar yaitu 30.03% disebabkan oleh pengukuran kecepatan angin dilakukan pada ketinggian yang berbeda dengan sensor BMKG. Pengukuran arah angin menunjukkan frekuensi arah angin yang berbeda karena tidak dilakukan setting titik referensi yang sama dengan sensor BMKG pada saat peletakan sensor. Pengukuran PM2.5 juga menunjukkan hasil kesalahan pengukuran cukup besar yaitu 14.7% karena peletakan sensor tidak dikondisikan dengan standar seperti pada sensor BMKG sehingga sampel partikulat yang diukur tidak sama dengan sensor BMKG. Untuk pengukuran intensitas cahaya dan kelembaban masing-masing telah menunjukkan trend yang sesuai dengan kodisi lingkungan. Pengujian pengiriman data juga berjalan dengan baik karena semua data yang tersimpan di database server berada pada kolom yang benar pada data base serta menunjukkan hasil yang sama dibandingkan dengan sampel data yang dibaca sensor secara manual.

Field monitoring and prediction on temperature distribution of glass curtain walls of a super high-rise building

Temperature action has a significant effect on the behavior of a supertall structure but has rarely been investigated due to lack of knowledge on the actual temperature measurements. It would be of great significance to predict the time-pace temperature distribution of a high-rise building by using the general environmental monitoring data which is more easily acquired and has a close relation with structural thermal action. In this study, the actual temperature distribution of the exterior glass curtain wall of Shanghai Tower is investigated using the field monitoring data achieved by the integrated real-time Structural Health Monitoring (SHM) system installed on the tower. The data recorded during 2018.1.1–2018.10.13 (283 days) are employed. A data-driven model is developed based on Artificial Neural Network (ANN), which is capable of predicting the temperature distribution of the curtain wall by using the environmental monitoring data including the wind velocity and direction measurements as well as the public weather information. Compared with the discrete temperature measurements at a few limited locations, the proposed model provides continuous time-space temperature distribution of the curtain wall, which adds insights into the knowledge of temperature actions for the maintenance of the Shanghai Tower and the design of glass curtain wall of a supertall structure.

Distributed sensing of wind direction using fiber-optic cables

AbstractIn the atmospheric boundary layer, phenomena exist with challenging properties such as spatial heterogeneity, particularly during stable weak wind situations. Studying spatially heterogeneous features requires spatially distributed measurements on fine spatial and temporal scales. Fiber-Optic Distributed Sensing (FODS) can provide spatially distributed measurements, simultaneously offering a spatial resolution on the order of decimeters and a temporal resolution on the order of seconds. While FODS has already been deployed to study various variables, FODS wind direction sensing has only been demonstrated in idealized wind tunnel experiments. We present the first distributed observations of FODS wind directions from field data. The wind direction sensing is accomplished by using pairs of actively heated fiber optic cables with cone-shaped microstructures attached to them. Here we present three different methods of calculating wind directions from the FODS measurements, two based on using combined wind speed and direction information and one deriving wind direction independently from FODS wind speed. For each approach, the effective temporal and spatial resolution is quantified using spectral coherence. With each method of calculating wind directions, temporal resolutions on the order of tens of seconds can be achieved. The accuracy of FODS wind directions was evaluated against a sonic anemometer, showing deviations of less than 15° most of the time. The applicability of FODS for wind direction measurements in different environmental conditions is tested by analysing the dependence of FODS wind direction accuracy and observable scales on environmental factors. Finally, we demonstrate the potential of this technique by presenting a period that displays spatial and temporal structures in the wind direction.

Electricity production and comparative analysis for wind availability power potential assessment at four sites in Egypt

This research represents the first wind energy potential assessment that covers major provinces in Egypt. The paper investigates a realistic study technically and economically of wind energy as a talented renewable source for electricity production of various regions in Egypt. More accurate prediction and measurement of wind speed and direction allow wind plants to supply clean, renewable power to businesses, and homeowners at lower costs. Wind resource assessments must be precise in order for wind farms to be built successfully. Wind resource assessments have been carried out in this study. Wind resources evaluation and precise assessment of wind capacity for the four selected sites in Egypt’s provinces from 2017 for 3 years at 10, 50 m above ground level (AGL): Hurgada, Aswan, Alexandria, and the capital of Egypt (Cairo). The wind speed data is taken from NASA for different sites in Egypt. The average annual wind speed was estimated to be 4.44, 4.31, 4.91, and 3.9 m/s at 10 m height, respectively. The economical factors such as NPC and COE in the selected regions are estimated. The optimum location for wind assessment in Egypt is Alexandria which gives maximum wind speed, maximum annual energy, minimum levelized cost of energy, and highest capacity factor. The proposed wind assessment will generated 20,1729 kWh of electricity per year and electricity generation cost per kWh/$ is 0.0818844. This planned cost of wind electric generation is compatible with the local electricity tariff. Also, Feasibility of Construction small wind turbine in this site is investigated. In addition, a criterion of wind farm site selection is presented here with Environmental Impact Assessment (EIA) study through Birds Migration aspect that decreases with increase turbine tower length and short blade length. Through reducing Egypt’s domestic fossil fuel consumption, this work will potentially save tons of carbon emissions each year.

Measurements of Wind Speed and Direction with an Aerosol UV Lidar

Measurements of Wind Speed and Direction with an Aerosol UV Lidar

Novel strategy for wide-range wind vector measurement using the hybrid CP/CTD heating mode and sequential measuring and correcting

To improve the performance of wind sensors in the high velocity range, this paper proposes a wind measurement strategy for thermal wind velocity sensors that combines the constant power and constant temperature difference driving modes of the heating element. Based on the airflow distribution characteristics from fluid dynamics, sequential measurement and correction is proposed as a method of measuring wind direction. In addition, a wind velocity and direction measurement instrument was developed using the above-mentioned approaches. The test results showed that the proposed instrument can obtain large dynamic wind velocity measurements from 0 to 60 m/s. The wind velocity measurement accuracy was ±0.5 m/s in the common velocity range of 0-20 m/s and ±1 m/s in the high velocity range of 20-60 m/s. The wind direction accuracy was ±3° throughout the 360° range. The proposed approaches and instrument are not only practical but also capable of meeting the requirements of wide-range and large dynamic wind vector measurement applications.