Wind Speed Measurements Research Articles

Power Prediction of Tandem Wind Rotor: Applied on Different Climatic Zones

The present study highlights a comparative numerical simulation of two geometric configurations of a three-bladed vertical axis wind rotor. The simulation was carried out for different wind speeds of the last year 2021 and applied on different climatic zones, Tiaret from Algeria and Nouadhibou from Mauritania. This simulation is useful to estimate the aerodynamic power of a new three-bladed tandem (biplane) geometry. The objective of this study is to predict the lift and the power coefficients in order to estimate the efficiency, according to the blades active surfaces of the two designs, monoplane three-bladed and three-bladed in tandem configurations. The results obtained by the simulation show relatively a significant agreement with the experiences and the 6DOF approach published previously. The efficiency of the tandem rotor is higher than the monoplane three-bladed rotor (Cp max = 0.46). The climatic zone of Nouadhibou (Mauritania) is the windiest during the year 2021 referring to the variation of the average measured wind speeds comparing to Tiaret zone. The installation of this new type of design at the zone of Nouadhibou is preferable in terms of the highest produced aerodynamic power

A control strategy for variable‐speed variable‐pitch wind turbines within the regions of partial‐ and full‐load operation without wind speed feedback

AbstractIn this paper, the control of a variable‐speed variable‐pitch wind turbine in the whole wind speed range is addressed, without any feedback measurement of wind speed. In addition to an aerodynamic torque observer able to ensure the tracking of the maximum delivered power in the partial‐load region, a novel wind speed observer is proposed for power regulation in the full‐load region, along with a sliding surface ensuring finite‐time set‐point stabilization of the speed tracking error. The proposed control solution has been validated on the National Renewable Energy Laboratory 5‐MW three‐blade wind turbine model using the recognized high‐fidelity simulation tool FAST.

Short-Term Prediction of 80–88 km Wind Speed in Near Space Based on VMD–PSO–LSTM

The accurate prediction of atmospheric wind speed in near space is of importance for both middle and upper atmospheric scientific research and engineering applications. In order to improve the accuracy of short-term wind speed predictions in near space, this paper proposes a multi-step hybrid prediction method based on the combination of variational modal decomposition (VMD), particle swarm optimization (PSO) and long short-term memory neural networks (LSTM). This paper uses the measurement of wind speed in the height range of 80–88 km at the Kunming site (25.6° N, 103.8° E) for wind speed prediction experiments. The results show that the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of multi–step wind predictions are less than 6 m/s and 15%, respectively. Furthermore, the proposed VMD–PSO–LSTM method is compared with the traditional seasonal difference autoregressive sliding average model (SARIMA) to investigate its performance. Our analysis shows that the percentage improvement of prediction performance compared to the traditional time series prediction model can reach at most 85.21% and 83.75% in RMSE and MAPE, respectively, which means that the VMD–PSO–LSTM model has better accuracy in the multi-step prediction of the wind speed.

Wind speed and direction measurement method based on intelligent optimization algorithm.

Accurate and rapid measurement of wind speed and direction is an important research topic. However, the current measurement algorithms based on ultrasonic arrays are constrained by the large computational effort caused by the spectrum peak search, which hinders the development and application of ultrasonic array wind parameter measurement technology. To overcome this problem, this study applies an intelligent optimization algorithm for measuring wind speed and direction based on a co-prime arc ultrasonic array, which avoids the problem of a large number of calculations in the spectrum peak search. First, the spatial-spectral function of the propagator method algorithm is employed as the fitness function of the particle swarm optimization algorithm. Then, the wind parameter estimation problem is formulated as a function optimization problem, which realizes the fast and accurate measurement of wind speed and direction. Then, the artificial bee colony algorithm is used to measure wind speed and direction, further reducing the calculation amount of the wind parameter measurement. The performance and speed of the proposed method are verified by the design simulation and comparison experiments, reducing the time complexity by up to 90%. In addition, the feasibility of the proposed method is validated in hardware experiments.

Short-term Wind Speed Prediction Based on Improved Auto Encoder

In order to improve the wind power consumption level of the new power system with new energy as the main body, it is necessary to accurately predict the wind speed. The key is to refine the dynamic trend of the wind power system and the potential physical structure in the wind speed sequence. Firstly, on the basis of the library Koopman dynamics theory and the encoder structures, a physically constrained spatio-temporal neural network is built, which generates the linear evolution moment of the nonlinear variables of the wind farm. Secondly, the system trend is approximated by the linear evolution matrix, and the forward and backward dynamics are fully considered in the prediction process. Then, the bidirectional correlation prediction mechanism and the cost function adapted to different objects are set to reduce the requirements for the reversibility and stability of the prediction sequence. Meanwhile, the hidden vector of feature space is visualized to show the feature interval dependency in the system. Finally, the effectiveness of the proposed method is verified by the wind speed measurement data of Prince Mountain in Beipiao. The results show that the proposed method has high prediction accuracy, strong generalization ability, and high interpretability for strong random and strong fluctuation wind speed series.

Validation of wind measurements from a multirotor RPAS-mounted ultrasonic wind sensor using a ground-based LiDAR system

The aim of this research was to establish the validity of wind measurements from on board a multirotor Remotely Piloted Aircraft System (RPAS) for the purposes of wind monitoring applications. A custom-built hexacopter RPAS recorded wind speed and direction by means of an onboard ultrasonic wind sensor, whilst operating in the inherently highly stochastic nature of open field atmospheric conditions. Experimental data were collected during open field hovering flights subject to different ambient conditions with free stream horizontal wind speeds reaching up to 12 m/s. Flights were conducted at different altitudes above ground level and in proximity to a Light Detection and Ranging (LiDAR) remote wind measurement unit that was used as a low-resolution reference meteorological station. Very good correlation was obtained between the RPAS and LiDAR unit for both wind speed and wind direction measurements across all hovering flight altitudes. The RPAS-based wind speed measurements were found to have a consistent 1 m/s positive offset, whilst the RPAS-based wind direction readings had a 6.16° negative offset. These were potentially caused by differences in the localized wind fields between the LiDAR and RPAS measuring positions, as well as by localized RPAS rotor-induced air flows for wind speed measurements and potential slight misalignments in the instruments’ reference datum for wind direction readings.

A comparative study of three methods to detect the upper atmospheric wind speed by DASH

The DASH (Doppler asymmetric spatial heterodyne) is used to detect the upper atmospheric wind speed by its imaging Fizeau interference fringes. There are two wind measurement methods: Fourier series method (FSM) and popular Fourier transform method (FTM). However, the wind speed measurement accuracy of FTM is greatly influenced by window function, and the calculation is relatively complicated. The Four-point algorithm (FPA) for DASH’s wind speed measurement is proposed in this paper. The contents of wind speed measurement principle, forward modeling, noise and inversion by the FSM, FTM and FPA are wholly compared and studied. The three wind speed measurement methods are all derived from the phase difference transformation of DASH Fizeau interference fringes. The Fizeau interference fringes with wind speed of 0–100 m/s at the interval of 10 m/s are simulated, and the forward wind speeds are obtained by FSM, FTM and FPA, and the corresponding wind measurement errors are 2.93%, 4.67% and 3.00%, respectively. After artificially adding Gaussian noise with a mean value of 0 and a standard deviation of 0.1, FSM, FTM and FPA are used to forward the Fizeau interference fringes after flat field, and the corresponding relative errors are 2.30%, 11.66% and 2.27%, respectively. After artificially adding Gaussian noise, the Fizeau interference fringes of wind speeds of 31–39 m/s with 1 m/s interval and 30.1–30.9 m/s with 0.1 m/s interval are simulated, and the forward wind speeds are obtained by FSM and FPA. In both cases, the wind speed measurement errors of FSM are 3.55% and 4.15% higher than those of FPA. The O(<sup>1</sup>S) 557.7 nm airglow at peak altitude of 98 km in Xi’an was photographed by using our GBAII (ground based airglow imaging interferometer)-DASH, and the imaging interferograms with zenith angles of 0° and 45° were obtained. Then by the methods of Fourier series, Fourier transform and FPA are used to obtain the inversion wind speed of 32.21 m/s, 43.55 m/s and 32.17 m/s, respectively. From the forward and inversion results of DASH, we can see that the FPA has a better result for detecting the upper atmospheric wind due to its simple calculation and smaller wind measurement error.

Statistical analysis of climate measurements for resilient design in social housing

Climate change is a progressive phenomenon that, although it can be mitigated, it cannot be stopped. As a consequence, the level of uncertainty is high to propose resilient design strategies to counteract its effects on people's comfort and health. Likewise, a limitation is the collection of information and access to climatic data of each place, so in the present work a cycle of temperature, relative humidity and wind speed measurements was proposed in a case study of social housing in the city of Tunja, Colombia. Thus, the objective of the article is to analyze and validate the reliability of the data collected on site. For this purpose, a descriptive statistical analysis of the information was carried out with tools such as Excel and PSPP version 1.4.1. As a result, it was found that, although the average level of reliability is acceptable, there are data below the minimum value of acceptability. It was concluded that descriptive statistics allows to know the margin of error to which the data are exposed and therefore reduce the uncertainty in the resilient design

A Bagged-Tree Machine Learning Model for High and Low Wind Speed Ocean Wind Retrieval From CYGNSS Measurements

This paper presents two empirical models, the low wind bagged trees (LWBT) and high wind bagged trees (HWBT) ensemble models to estimate ocean surface wind speed using spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data. The models are empirically trained using NASA’s Cyclone GNSS (CYGNSS) mission level 1 data (version 2.1). The truth label for the LWBT model is the wind speed product derived from European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 and Global Data Assimilation System (GDAS), while the label for the HWBT model is wind speed measurements from Stepped Frequency Microwave Radiometer (SFMR). Testing results show that the LWBT and HWBT models achieved global wind speed retrieval root-mean-square-error (RMSE) of ~1.5 m/s and ~1.4 m/s, respectively, corresponding to an improvement of 29% and 65% with respect to the CYGNSS Level 2 standard wind speed product. The maximum bias is reduced by 65% and 60% for LWBT and HWBT over the Level 2 wind speeds, respectively. Two typhoon case studies are presented to corroborate the model performances and their retrieved wind speeds are consistent with reports from World Meteorological Organization (WMO) and with the measurement provided by the Huangmao Zhou (HMZ) weather station.

Wind Speed and Direction Measurement Based on Three Mutually Transmitting Ultrasonic Sensors

The existing ultrasonic anemometer is influenced by the ambient temperature and humidity, shadow effect, and has weak suppression ability for fixed frequency interference, which directly affects the measurement accuracy of the ultrasonic anemometer. In this letter, we propose a three mutually transmitting ultrasonic sensors wind measurement method, which uses a three mutually transmitting ultrasonic sensors array structure combined with the cyclic correlation time delay estimation algorithm for ultrasonic transmission time estimation, and then obtains wind speed and direction values based on the relationship between ultrasonic transmission time and wind vector. The proposed method can effectively reduce the effects of temperature, humidity and shadowing effects on wind vector measurements, suppress interference at different frequencies, and reduce the computational complexity of the time delay estimation algorithm. Finally, we perform simulation experiments to verify the superiority of the proposed algorithm. At the same time, the validity of the proposed method is verified by actual measurement experiment of the three mutually transmitting ultrasonic wind measurement system built. The relative error of wind speed measurement is 3% and the error of wind direction measurement is 3° in the actual measurement environment.

An iterative data-driven learning algorithm for calibration of the internal model in advanced wind turbine controllers

Modern industrial wind turbine controllers for partial-load region control are becoming increasingly complex by progressively relying on modeled aerodynamic characteristics. These advanced turbine controllers generally consist of a combined wind speed estimator and tracking controller, allowing for a granular trade-off between energy capture maximization and (fatigue) load minimization. Because of the limited measurements available to the controller, the control scheme's internal model quality is of utmost importance in satisfying performance and stability requirements. Therefore, the calibration thereof is of particular interest. To date, little work has been performed on the direct calibration of the model information. This work proposes a data-driven iterative learning algorithm for calibrating the internal physical model parameters. The learning algorithm uses generally available closed-loop turbine measurements, complemented with an external measurement of the rotor effective wind speed (REWS), and is thereby largely nondisruptive. The algorithm is based on steady-state assumptions and performs iterative batch-wise updates of the internal control model toward convergence. As the algorithm corrects at the actual turbine operating point, short-term relocations of the turbine's operating point can be used to calibrate in a broader operational domain. Results show outstanding learning capabilities for an aerodynamically degraded wind turbine under realistic turbulent wind conditions. Moreover, a sensitivity study is performed to expose the algorithm's susceptibility to measurement errors, algorithm tuning, and the size of the data set.

Environmental conditions during hot summer day in Belgrade - interaction of thermal risk and noise pollution

In urban environments, people's exposure to harmful ambient conditions and noise represents a particular challenge in the modern world. Global climate change and increasing population density, along with the increasing number of vehicles in urban areas, are constantly exacerbating this problem. The idea of this study is to assess the effects of temperature risks and noise pollution in two microlocations. An analysis of the ambient conditions and noise in the urban environment was conducted at two locations in Belgrade, which differed in configuration. Simultaneous measurements of temperature (Ta and Tg), humidity, wind speed, and equivalent noise level were taken during a hot summer day (maximum temperature over 30?C). It was shown that the noise level exceeds the prescribed noise level values for the analyzed acoustic zone. The noise level values differ for the two locations due to the different traffic densities in the environment and different urban characteristics. This study showed that the concept of green space, but also of built-up type in urban planning could has importance impact in regulating thermal and noise conditions and obtained improved urban environments.

The relationship between wind speed and satellite measurements of fire radiative power

Background Satellite fire radiative power (FRP) products are a potential source of wildland fire growth measurements that could be used with gridded or observed weather data to study meteorological influences on wildfire behaviour. There is little research to date examining how FRP relates to weather. Aims The goal of this study is to explore the relationship between satellite wildland fire FRP measurements and wind speed. Methods We examine GOES-16 FRP data from the summer of 2018 in California, comparing it with winds from the CANSAC reanalysis to understand the relationship between wind and FRP. Examination focuses on statistical distribution of the data and results of two different approaches to relating FRP and wind speed. Key results FRP is log-normally distributed, and there are significant consequences to using conventional statistical techniques that assume normality. We propose an alternative framing of the wind speed–FRP relationship to preserve more information about the FRP distribution. Conclusions Use of FRP data for meaningful understanding of fire–weather interactions requires attention to the nature of the FRP empirical distribution. Implications Applying our alternative approach to relating wildland fire FRP to weather has the potential to retain much more information than conventional approaches, but requires careful examination of the FRP data in each study.

Влияние агротехнических мероприятий и мелиоративных защитных лесных насаждений на дефляцию почв Ростовской области

Purpose: to study the deflation manifestation patterns on Rostov region soils in the cold and warm periods of the year, depending on the agricultural background, the projective cover of soil surface by plants and the availability of reclamation protective forest plantations. Materials and methods. Deflation studies were being carried out on the soils of Aksai and Orlovsky districts Rostov region from 1970 to 2022. Conventional research methods including the measurement of wind speed by anemometers, were used, the soil layer displaced by the wind was determined by the method of pins and dust collectors of authors own design. Results. During the period of dust storm from April 14 to April 21, 2003, the influence of the position and amount of stubble on soil removal under various methods of tillage was determined. During chisel tillage, the largest amount of stubble was noted – 250 pcs/sq. m, including the standing one – 110 pcs/sq. m and lying one of 140 pcs/sq. m. At the same time, there was a decrease in wind velocity from 10.9 m/s at a height of 0.8 m to 3.2 m/s at a height of 0.05 m. With more than 120 stubble pcs/sq. m remaining on the soil surface, the soil is protected from wind, and the removal of fine earth in these options is zero. The projective soil surface cover by plants is the next factor influencing deflation. When it decreases, the soil remains unprotected from wind gusts and soil removal increases, respectively, from 1 t/ha from a perennial grasses field up to 40–70 t/ha by non-moldboard plowing and up to 260–297 t/ha by moldboard plowing. Conclusions. To reduce the loss of fertile soil layer from deflation, it is recommended to carry out the basic tillage, leaving stubble and crop residues on soil surface, which reduce the wind velocity near the soil surface and prevent deflation.

Linearity and Sensitivity Analysis of MEMS Thermal Wind Sensor via Analytical Model

In this article, an analytical model focusing on the linearity and sensitivity of micro-electromechanical systems (MEMS) thermal wind sensors with low thermal conductivity substrate (LTCS) and packaging is proposed. The influence of substrate material, substrate thickness, heater size, measurement distance, packaging material, and packaging size on the sensor performances are investigated to serve as design guidelines for future thermal wind sensors. For the first time, model results reveal that all the factors listed above have opposite effects on the sensitivity and linearity of the wind sensors. Furthermore, sensitivity and linearity are related to power consumption and measurement range respectively. Therefore, a compromise between sensitivity and linearity is required for high wind speed measurement applications. The real wind sensor fabricated with ceramic substrate and encapsulated by a plastic carrier was tested in a wind tunnel. The experimental results illustrated that the sensitivity of the sensor at 3.3 m/s is 157 mV/( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{W}\cdot \text{m}\cdot \text{s}^{-1}$ </tex-math></inline-formula> ). In addition, the fitting linearity parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$B_{m}$ </tex-math></inline-formula> is 0.424 in the range from 0 to 33 m/s.

Heteroscedastic Bayesian Optimisation for Active Power Control of Wind Farms*

Active power control of wind farms remains an open challenge due to inherent noise in wind power that arises from uncertain wind speed measurements and plant/model mismatch. To leverage the heteroscedastic nature of the wind power noise, heteroscedastic Bayesian optimisation (BO) is used for active power control of wind farms. BO utilises closed-loop performance data to tune the parameters of a stochastic model predictive controller (SMPC) in a systematic and data-efficient manner. This, in turn, allows for enhancing the closed-loop performance of the controller intended to decrease the power tracking error. A case study with 9 turbines in a 3 × 3 wind farm shows that the heteroscedastic BO approach achieves a reduced closed-loop power tracking error in terms of root-mean-square by 8.89% compared to one that relies on nominal BO and a decrease by 64.99% compared to a nominal model predictive controller (MPC) whose performance is not tuned using closed-loop data and BO.

Investigation of the impact of integrating renewable sources with variable power generation, taking into account restrictions on the voltage stability limit

This article is devoted to the development of a methodology for estimating the amount of balancing power in a system with a large share of renewable energy sources (RES). For this purpose, an analysis was made of the influence of an increase in the installed capacity of wind power plants (WPPs) on the value of the balance power of the energy system, random characteristics of its variability for various combinations of the number of workers in the WPP system. The possibilities of realizing the distribution of balance power in characteristic critical loaded modes of the system are considered. Using the example of a real power system, the wind speed measurement data obtained for the geographic locations of wind stations on the power systems using the developed programs, simulations of the system modes were carried out for various combinations of these stations. The dependences for the balancing power are constructed, the conditions for reducing this power, as well as the conditions for its limitation on the stability of the voltage in the balancing part of the system, are determined.

Mixing Characteristics and Flow Fields Optimization of Primary Air for Coal Mill

This paper mainly focuses on the problems of inaccurate measurement of the primary air flow rate in a coal-fired power plant. The feasibility of improving the uniformity of flow fields and temperature fields by installing double-layer arch cool air distributor and mixed flow matrix was analyzed by the method of numerical simulation. The results show that, the relative standard deviation of the wind speed measurement section of optimization model decreased from 18.7%~18.4% to 9%~9.1%, compared with the original model. Similarly, the relative standard deviation of temperature measurement section also decreased from 15%~14.6% to 1.3%~1.4%. The study suggests that this mixed flow fields equalization solution can effectively enhance the mixing of cool and hot primary air, and can improve the uniformity of the mixed air flow fields, thereby improving the accuracy and reliability of primary air measurement.

A deep learning assisted adaptive nonlinear deloading strategy for wind turbine generator integrated with an interconnected power system for enhanced load frequency control

• Implementation of DL based RNN-LSTM approach for wind speed forecasting. • DL assisted adaptive nonlinear deloading strategy for frequency support from WTGs. • Fractional-order fuzzy-based PID control structure as supplementary controller. • Real-time validation of proposed scheme on OPAL-RT 4510 platform. The existing linear and quadratic deloading strategies with constant deloading factor, fail to effectively handle the nonlinear characteristics of WTGs. This work proposes a novel deep learning assisted adaptive nonlinear deloading (DL-AND) methodology based on a Newtonian interpolated polynomial for WTG integrated with an interconnected power system to provide effective load frequency control (LFC). The key feature of the proposed technique is its ability to adapt the deloading factor in accordance with wind speed to optimize the reserve power margin of the WTG. In this work, a deep learning-based recurrent neural network (RNN) with long short-term memory (LSTM) technique has been proposed for wind speed forecasting, as using a wind speed measurement device is expected to incorporate measurement lag, leading to the deterioration of the deloading operation. The proposed novel DL-AND technique for WTGs is used along with a fractional-order fuzzy-based PID (FFOPID) control structure as a supplementary controller for handling uncertainties in order to provide effective LFC. Further, Exhaustive simulation studies have been carried out to investigate the proposed technique and results show the effectiveness of proposed novel DL-AND strategy with FFOPID in terms WTG reserve power margin, frequency support and performance index for all the considered case studies.

Wind speed estimation for the correction of wind-caused errors in historical precipitation data

The wind has a significant impact on the accuracy of precipitation measurement in the case of collecting gauges. As widely known, the velocity field of wind suffers a deformation over and around the precipitation gauges, which causes deviations in the measured quantities. This error must be corrected if it is possible. Thanks to numerous researches, correction formulas give tools for adjusting precipitation data in the function of the wind speed and raindrop distribution (DSD) relationship, gauge parameters, and for the case of snow and temperature. The measured intensity of precipitation in historical data allows estimating the DSD, but in most cases, there are no simultaneously measured wind speed data coupled to the historical precipitation data. Characteristic data of wind speed can be estimated based on the wind speed statistics, and these data can be utilized for the statistical correction of the precipitation measurements. The statistical correction means that the rainfall data can be adjusted with the expected value of the wind speed for a more extended observation period, assuming a stationarity of wind speed statistics for the given location. After the statistical correction, the unique data will not be unbiased, but statistically they will be closer to the actual value, and the correction will be statistically correct in inherited perecipitation cheracteristics, as for example the IDF curves. For this correction, an investigation is necessary to find the adequate wind statistics for the rainfall correction. This paper shows the results of a study about the relation of statistics of wind speeds during precipitation, based on a 10-minute sampling period. The wind speed data were independent of the rain depth (or intensity) data. The result of the study shows that the distribution of wind speeds differs of the wind speed distribution measured in the precipitation events. This difference can be treated easily using the stable rate of the means of these distributions. This result gives a step toward correcting the wind-affected error of historical precipitation data.