F(P)SO Global Responses in the West of Africa Squall Environment

2005 ◽  
Author(s):  
Zhibin Zhong ◽  
Yong Luo ◽  
Dusan Curic
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Direction ◽  
Time History ◽  
The Past ◽  
Transient Nature ◽  
Time Histories ◽  
The Time Domain ◽  
Water Depths ◽  
Tandem Offloading

Mooring design for F(P)SOs in West of Africa offshore environment is in many cases governed by the squall driven condition. In the past, the squall condition was typically analyzed by using the peak wind speed with associated wind direction. However, due to its inherent transient nature, the squall event formulated in the time history with varying wind speed and direction is more appropriate and could be potentially more critical for the mooring system design. This approach has been adopted in the design and analysis of recent F(P)SO mooring systems. The F(P)SOs are turret-moored in various water depths in offshore West of Africa. A series of squall time histories have been applied to predict the global responses of the F(P)SO in the time domain. Each squall time history, which provides a unique combination of wind speed and direction variations, is analyzed in five nominal directions covering a sector of 90 degrees from East to West. Squall time histories are also applied to analyze the tandem offloading operation. The results are compared with those of the conventional constant wind speed approach and a few interesting observations are made. The paper also provides some insights into the F(P)SO yaw motions, as well as their relations to the changing wind direction. Analysis results demonstrate that using the squall time series with changing wind speed and direction is more critical than the conventional constant wind speed approach in the tandem offloading scenario. It is therefore recommended that mooring analysis using squall time series should at least be used for the tandem offloading simulations.

Simulation of Time Histories of Second-Order Wave Effects by Pseudo-Linear Transformation

1995 ◽  
Vol 117 (2) ◽  
pp. 78-84
Author(s):  
Y. Li
Keyword(s):  
Dynamic Response ◽  
Time History ◽  
Second Order ◽  
Hilbert Transforms ◽  
Structural Dynamic ◽  
Quadratic Transformation ◽  
Wave Effects ◽  
Time Histories ◽  
Component Wave ◽  
The Time Domain

Simulation of the time histories of second-order wave effects is often performed by quadratic transformation of a wave time history. By the present approach, the quadratic transformation of waves is approximated by linear combinations of the products of component wave time records and their Hilbert transforms. The computational efficiency is greatly enhanced. The efficient quadratic transformation of a time history is for the time domain solution of structural dynamic response, and can also be used as a post-processor of the frequency domain solution for obtaining statistic parameters of dynamic response.

Displacement Time Histories by Direct Numerical Integration of Acceleration Data

1991 ◽  
Author(s):  
M. L. Wang ◽  
S. R. Subia
Keyword(s):  
Time History ◽  
Shear Walls ◽  
Model Tests ◽  
Seismic Events ◽  
Scaled Model ◽  
Acceleration Data ◽  
Time Histories ◽  
Seismic Loadings ◽  
The Time Domain ◽  
Direct Numerical Integration

Abstract Acceleration measurements often provide the engineer with a means by which to determine the forces within dynamic structural systems. However for certain problems, information about the structural motion, the displacement-time history, may also be of interest. One such application deals with the evaluation of stiffness in reinforced concrete structures during seismic events. Scaled model tests of these events suggest that the stiffness of these structures often degrades drastically. The displacement response of these seismic events are required both for the development and evaluation of postulated structural stiffness models. This paper discusses the processing of acceleration data from scaled model tests to obtain displacement-time histories for low aspect shear walls subject to simulated seismic loadings. Displacement histories obtained in the time domain are compared with those produced using a frequency domain system identification analysis.

scholarly journals A "Global Radiosonde and tracked-balloon Archive on Sixteen Pressure levels" (GRASP) going back to 1905 – Part 2: homogeneity adjustments for pilot balloon and radiosonde wind data

2014 ◽  
Vol 6 (2) ◽  
pp. 297-316 ◽  
Author(s):  
L. Ramella Pralungo ◽  
L. Haimberger
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Direction ◽  
Sampling Bias ◽  
Observation Time ◽  
Early Years ◽  
Wind Speed Data ◽  
Wind Speeds ◽  
Measurement Biases ◽  
The 1960S

Abstract. This paper describes the comprehensive homogenization of the "Global Radiosonde and tracked balloon Archive on Sixteen Pressure levels" (GRASP) wind records. Many of those records suffer from artificial shifts that need to be detected and adjusted before they are suitable for climate studies. Time series of departures between observations and the National Atmospheric and Oceanic Administration 20th-century (NOAA-20CR) surface pressure only reanalysis have been calculated offline by first interpolating the observations to pressure levels and standard synoptic times, if needed, and then interpolating the gridded NOAA-20CR standard pressure level data horizontally to the observation locations. These difference time series are quite sensitive to breaks in the observation time series and can be used for both automatic detection and adjustment of the breaks. Both wind speed and direction show a comparable number of breaks, roughly one break in three stations. More than a hundred artificial shifts in wind direction could be detected at several US stations in the period 1938/1955. From the 1960s onward the wind direction breaks are less frequent. Wind speed data are not affected as much by measurement biases, but one has to be aware of a large fair-weather sampling bias in early years, when high wind speeds were much less likely to be observed than after 1960, when radar tracking was already common practice. This bias has to be taken into account when calculating trends or monthly means from wind speed data. Trends of both wind speed and direction look spatially more homogeneous after adjustment. With the exception of a widespread wind direction bias found in the early US network, no signs of pervasive measurement biases could be found. The adjustments can likely improve observation usage when applied during data assimilation. Alternatively they can serve as a basis for validating variational wind bias adjustment schemes. Certainly, they are expected to improve estimates of global wind trends. All the homogeneity adjustments are available in the PANGAEA archive with associated doi:10.1594/PANGAEA.823617.

scholarly journals A global radiosonde and tracked balloon archive on 16 pressure levels (GRASP) back to 1905 – Part 2: Homogeneity adjustments for PILOT and radiosonde wind data

2014 ◽  
Vol 7 (1) ◽  
pp. 335-383 ◽  
Author(s):  
L. Ramella Pralungo ◽  
L. Haimberger
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Direction ◽  
Sampling Bias ◽  
Observation Time ◽  
Early Years ◽  
Wind Speed Data ◽  
Wind Speeds ◽  
Measurement Biases ◽  
The 1960S

Abstract. This paper describes the comprehensive homogenization of the GRASP wind records. Many of those records suffer from artificial shifts that need to be detected and adjusted before they are suitable for climate studies. Time series of departures between observations and the National Atmospheric and Oceanic Administration 20th century (NOAA-20CR) surface pressure only reanalysis have been calculated offline by first interpolating the observations to pressure levels and standard synoptic times, if needed, and then interpolating the gridded NOAA-20CR standard pressure level data horizontally to the observation locations. These difference time series are quite sensitive to breaks in the observation time series and can be used for both automatic detection and adjustment of the breaks. Both wind speed and direction show a comparable number of breaks, roughly one break in three stations. More than hundred artificial shifts in wind direction could be detected at several US stations in the period 1938/1955. From the 1960s onward the wind direction breaks are less frequent. Wind speed data are not so much affected by measurement biases but one has to be aware of a large fair weather sampling bias in early years when high wind speeds were much less likely to be observed than after 1960 when RADAR tracking was already common practice. It has to be taken into account when calculating trends or monthly means from wind speed data. Trends of both wind speed and direction look spatially more homogeneous after adjustment. With the exception of a widespread wind direction bias found in the early US network no signs of pervasive measurement biases could be found. The adjustments can likely improve observation usage when applied during data assimilation. Alternatively they can serve as basis for validating variational wind bias adjustment schemes. Certainly they are expected to improve estimates of global wind trends. All the homogeneity adjustments are available in the PANGAEA archive with the associated DOI doi:10.1594/PANGAEA.823617.

Short-Term Wind Speed Combination Prediction Model of Neural Network and Time Series

2012 ◽  
Vol 608-609 ◽  
pp. 764-769
Author(s):  
Hao Zheng ◽  
Jian Yan Tian ◽  
Fang Wang ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Prediction Model ◽  
Wind Direction ◽  
Prediction Accuracy ◽  
Wind Farm ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Input Variables

This paper uses neural network combined with time series to establish rolling neural network model to predict short-term wind speed in the wind farm. In order to improve wind speed prediction accuracy, this paper analyzes effects of wind direction on wind speed by gray correlation analysis and obtains the correlation coefficient between wind speed at next moment and current wind direction is the largest by calculating. Then wind direction at current moment, historical wind speed and residuals which determined by time series are used as input variables to establish wind prediction model with rolling BP neural network. The simulation results show that neural network combined with time series which considers wind direction could improve the prediction accuracy when wind speed fluctuation is large.

Prediction of Atmospheric Turbulence Characteristics for Surface Boundary Layer using Empirical Spectral Methods

2020 ◽  
Author(s):  
Yagya Dutta Dwivedi ◽  
Vasishta Bhargava Nukala ◽  
Satya Prasad Maddula ◽  
Kiran Nair
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Surface Roughness ◽  
Wind Speed ◽  
Atmospheric Turbulence ◽  
Surface Boundary ◽  
Low Frequencies ◽  
Surface Boundary Layer ◽  
Power Spectral ◽  
Mean Wind Speed

Abstract Atmospheric turbulence is an unsteady phenomenon found in nature and plays significance role in predicting natural events and life prediction of structures. In this work, turbulence in surface boundary layer has been studied through empirical methods. Computer simulation of Von Karman, Kaimal methods were evaluated for different surface roughness and for low (1%), medium (10%) and high (50%) turbulence intensities. Instantaneous values of one minute time series for longitudinal turbulent wind at mean wind speed of 12 m/s using both spectra showed strong correlation in validation trends. Influence of integral length scales on turbulence kinetic energy production at different heights is illustrated. Time series for mean wind speed of 12 m/s with surface roughness value of 0.05 m have shown that variance for longitudinal, lateral and vertical velocity components were different and found to be anisotropic. Wind speed power spectral density from Davenport and Simiu profiles have also been calculated at surface roughness of 0.05 m and compared with k−1 and k−3 slopes for Kolmogorov k−5/3 law in inertial sub-range and k−7 in viscous dissipation range. At high frequencies, logarithmic slope of Kolmogorov −5/3rd law agreed well with Davenport, Harris, Simiu and Solari spectra than at low frequencies.

scholarly journals The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters

2018 ◽  
Vol 7 (2) ◽  
pp. 139-150 ◽  
Author(s):  
Adekunlé Akim Salami ◽  
Ayité Sénah Akoda Ajavon ◽  
Mawugno Koffi Kodjo ◽  
Seydou Ouedraogo ◽  
Koffi-Sa Bédja
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Series Data ◽  
Empirical Method ◽  
Likelihood Method ◽  
Series Data ◽  
New Approach ◽  
Weibull Parameters ◽  
Distribution Histogram ◽  
Mean Wind Speed

In this article, we introduced a new approach based on graphical method (GPM), maximum likelihood method (MLM), energy pattern factor method (EPFM), empirical method of Justus (EMJ), empirical method of Lysen (EML) and moment method (MOM) using the even or odd classes of wind speed series distribution histogram with 1 m/s as bin size to estimate the Weibull parameters. This new approach is compared on the basis of the resulting mean wind speed and its standard deviation using seven reliable statistical indicators (RPE, RMSE, MAPE, MABE, R2, RRMSE and IA). The results indicate that this new approach is adequate to estimate Weibull parameters and can outperform GPM, MLM, EPF, EMJ, EML and MOM which uses all wind speed time series data collected for one period. The study has also found a linear relationship between the Weibull parameters K and C estimated by MLM, EPFM, EMJ, EML and MOM using odd or even class wind speed time series and those obtained by applying these methods to all class (both even and odd bins) wind speed time series. Another interesting feature of this approach is the data size reduction which eventually leads to a reduced processing time.Article History: Received February 16th 2018; Received in revised form May 5th 2018; Accepted May 27th 2018; Available onlineHow to Cite This Article: Salami, A.A., Ajavon, A.S.A., Kodjo, M.K. , Ouedraogo, S. and Bédja, K. (2018) The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. Int. Journal of Renewable Energy Development 7(2), 139-150.https://doi.org/10.14710/ijred.7.2.139-150

scholarly journals Evaluating the performance of multivariate indicators of resilience loss

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Els Weinans ◽  
Rick Quax ◽  
Egbert H. van Nes ◽  
Ingrid A. van de Leemput
Keyword(s):  
Time Series ◽  
Complex Systems ◽  
Multivariate Time Series ◽  
Tipping Points ◽  
Physical And Mental Health ◽  
One Dimensional ◽  
Slowing Down ◽  
The Past ◽  
Technological Developments ◽  
Resilience Indicators

AbstractVarious complex systems, such as the climate, ecosystems, and physical and mental health can show large shifts in response to small changes in their environment. These ‘tipping points’ are notoriously hard to predict based on trends. However, in the past 20 years several indicators pointing to a loss of resilience have been developed. These indicators use fluctuations in time series to detect critical slowing down preceding a tipping point. Most of the existing indicators are based on models of one-dimensional systems. However, complex systems generally consist of multiple interacting entities. Moreover, because of technological developments and wearables, multivariate time series are becoming increasingly available in different fields of science. In order to apply the framework of resilience indicators to multivariate time series, various extensions have been proposed. Not all multivariate indicators have been tested for the same types of systems and therefore a systematic comparison between the methods is lacking. Here, we evaluate the performance of the different multivariate indicators of resilience loss in different scenarios. We show that there is not one method outperforming the others. Instead, which method is best to use depends on the type of scenario the system is subject to. We propose a set of guidelines to help future users choose which multivariate indicator of resilience is best to use for their particular system.

scholarly journals Enhancing Wind Turbine Power Forecast via Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 261
Author(s):  
Tianyang Liu ◽  
Zunkai Huang ◽  
Li Tian ◽  
Yongxin Zhu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Rapid Development ◽  
Superior Performance ◽  
Learning Platform ◽  
Novel Approach ◽  
Wind Power Forecast ◽  
Transformation Methods

The rapid development in wind power comes with new technical challenges. Reliable and accurate wind power forecast is of considerable significance to the electricity system’s daily dispatching and production. Traditional forecast methods usually utilize wind speed and turbine parameters as the model inputs. However, they are not sufficient to account for complex weather variability and the various wind turbine features in the real world. Inspired by the excellent performance of convolutional neural networks (CNN) in computer vision, we propose a novel approach to predicting short-term wind power by converting time series into images and exploit a CNN to analyze them. In our approach, we first propose two transformation methods to map wind speed and precipitation data time series into image matrices. After integrating multi-dimensional information and extracting features, we design a novel CNN framework to forecast 24-h wind turbine power. Our method is implemented on the Keras deep learning platform and tested on 10 sets of 3-year wind turbine data from Hangzhou, China. The superior performance of the proposed method is demonstrated through comparisons using state-of-the-art techniques in wind turbine power forecasting.

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