Accuracy Of Wind Speed Estimates Research Articles

Effective wind speed estimation study of the wind turbine based on deep learning

Wind speed is the driver of wind turbines, and the precise estimate of that makes it possible to improve the control effects and the efficiency of energy production. This paper proposes a method of effective wind speed estimator that considers the variation in blade radius and reconstructs the mapping of the aerodynamic. First, the proposed method utilizes available data of the wind turbine to train two neural network models based on radial basis functions (RBF). The models estimate the effective radius and reconstruct the aerodynamic mapping surface, respectively. Then, on this basis, train a wind speed estimation model based on the Long Short-Term Memory (LSTM) neural network, which can effectively estimate the current wind speed in real-time and predict the wind speed of the next time step as the reference for the following estimation. In addition, the RBF model and LSTM model can be updated and improved adaptively based on new data to ensure the accuracy of an effective wind speed estimator. Finally, the proposed wind speed estimation method is compared with the existing methods. The experimental results show that the proposed method has strong anti-interference characteristics, and improves the effective wind speed estimation accuracy over 70% on average.

Method for Improvement of Ocean Wind Speed Estimation Accuracy by Taking into Account the Relation between Wind Speed and Wind Direction

A method for improvement of ocean wind speed estimation accuracy by taking into account the relation between wind speed and wind direction is proposed. Brightness temperature observed with microwave radiometer onboard satellite is modified with microwave radiometer derived wind direction proposed by Frank Wentz. Using the modified brightness temperature, more precise wind speed is estimated. Experiments with AMSR-E and NCEP GDAS data show improvements of wind speed estimation in comparison to the existing method based on the geophysical model of Frank Wentz together with the retrieval algorithm of Akira Shibata.

Developing and Testing Models for Sea Surface Wind Speed Estimation with GNSS-R Delay Doppler Maps and Delay Waveforms

This paper focuses on sea surface wind speed estimation based on cyclone global navigation satellite system reflectometry (GNSS-R) data. In order to extract useful information from delay-Doppler map (DDM) data, three delay waveforms are presented for wind speed estimation. The delay waveform without Doppler shift is defined as central delay waveform (CDW), and the integral of the delay waveforms with different Doppler shift values is defined as integral delay waveform (IDW), while the difference between normalized IDW (NIDW) and normalized CDW (NCDW) is defined as differential delay waveform (DDW). We first propose a data filtering method based on threshold setting for data quality control. This method can select good-quality DDM data by adjusting the root mean square (RMS) threshold of cleaned DDW. Then, the normalized bistatic radar scattering cross section (NBRCS) and the leading edge slope (LES) of IDW are calculated using clean DDM data. Wind speed estimation models based on NBRCS and LES observations are then developed, respectively, and on this basis, a combination wind speed estimation model based on determination coefficient is further proposed. The CYGNSS data and ECMWF reanalysis data collected from 12 May 2020 to 12 August 2020 are used, excluding data collected on land, to evaluate the proposed models. The evaluation results show that the wind speed estimation accuracy of the piecewise function model based on NBRCS is 2.3 m/s in terms of root mean square error (RMSE), while that of the double-parameter and triple-parameter models is 2.6 and 2.7 m/s, respectively. The wind speed estimation accuracy of the double-parameter and triple-parameter models based on LES is 3.3 and 2.5 m/s. The results also demonstrate that the RMSE of the combination method is 2.1 m/s, and the coefficient of determination is 0.906, achieving a considerable performance gain compared with the individual NBRCS- and LES-based methods.

Optimization of Airborne Antenna Geometry for Ocean Surface Scatterometric Measurements

We consider different antenna configurations, ranging from simple X-configuration to multi-beam star geometries, for airborne scatterometric measurements of the wind vector near the ocean surface. For all geometries, track-stabilized antenna configurations, as well as horizontal transmitter and receiver polarizations, are considered. The wind vector retrieval algorithm is generalized here for an arbitrary star geometry antenna configuration and tested using the Ku-Band geophysical model function. Using Monte Carlo simulations for the fixed total measurement time, we show explicitly that the relative wind speed estimation accuracy barely depends on the chosen antenna geometry, while the maximum wind direction retrieval error reduces moderately with increasing angular resolution, although at the cost of increased retrieval algorithm computational complexity, thus, limiting online analysis options with onboard equipment. Remarkably, the simplest X-configuration, while the simplest in terms of hardware implementation and computational time, appears an outlier, yielding considerably higher maximum retrieval errors when compared to all other configurations. We believe that our results are useful for the optimization of both hardware and software design for modern airborne scatterometric measurement systems based on tunable antenna arrays especially, those requiring online data processing.

A Spectra-Analysis-Based Algorithm for Wind Speed Estimation From X-Band Nautical Radar Images

In this letter, a new method for estimating wind speeds from X-band nautical radar images is presented. Wind speeds are determined from the wavenumber spectra of radar backscatter using a logarithmic relationship between the spectral strength and the wind speed. The method can be applied to both rain-contaminated and rain-free radar data. The method has been tested using shipborne X-band nautical data collected over the North Atlantic Ocean. A comparison with the anemometer data shows that the root mean square errors of wind speeds estimated from rain-contaminated radar data using the proposed method and that by Lund et al. are 1.6 and 7.5 m/s, respectively. The wind speed estimation accuracy is improved by 5.9 m/s with the new method.

Time Delay Estimation Algorithm Used in Ultrasonic Wind Speed Measurement System

In this work, the application of time delay estimation in ultrasonic wind speed measurement system is discussed. The algorithm of time delay estimation based on least mean square (LMS) adaptive noise cancellation of generalized correlation with fixed ultrasonic is presented. The algorithm uses LMS daptive filter to eliminate the noise interference efficiently and does not need to know prior-knowledge of signal and noise. Then, it uses generalized correlation to estimate the time delay. Computer simulation and practical test results show that the algorithm can effectively eliminate noise. The wind speed estimation accuracy is very high; so the algorithm can be used for monitoring the low-level wind field at airport regional.