The analysis of turbulence intensity based on wind speed data in onshore wind farms

2018 ◽  
Vol 123 ◽  
pp. 756-766 ◽  
Author(s):  
Guorui Ren ◽  
Jinfu Liu ◽  
Jie Wan ◽  
Fei Li ◽  
Yufeng Guo ◽  
...  
Keyword(s):  
Wind Speed ◽  
Turbulence Intensity ◽  
Wind Farms ◽  
Onshore Wind ◽  
Wind Speed Data

scholarly journals Frequency Distribution Model of Wind Speed Based on the Exponential Polynomial for Wind Farms

2019 ◽  
Vol 11 (3) ◽  
pp. 665 ◽  
Author(s):  
Lingzhi Wang ◽  
Jun Liu ◽  
Fucai Qian
Keyword(s):  
Wind Speed ◽  
Frequency Distribution ◽  
Least Squares Method ◽  
Wind Farms ◽  
Rayleigh Distribution ◽  
Distribution Model ◽  
Exponential Polynomial ◽  
Distribution Models ◽  
Wind Speed Data ◽  
Wind Speeds

This study introduces and analyses existing models of wind speed frequency distribution in wind farms, such as the Weibull distribution model, the Rayleigh distribution model, and the lognormal distribution model. Inspired by the shortcomings of these models, we propose a distribution model based on an exponential polynomial, which can describe the actual wind speed frequency distribution. The fitting error of other common distribution models is too large at zero or low wind speeds. The proposed model can solve this problem. The exponential polynomial distribution model can fit multimodal distribution wind speed data as well as unimodal distribution wind speed data. We used the linear-least-squares method to acquire the parameters for the distribution model. Finally, we carried out contrast simulation experiments to validate the effectiveness and advantages of the proposed distribution model.

scholarly journals A New Wind Speed Forecasting Modeling Strategy Using Two-Stage Decomposition, Feature Selection and DAWNN

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 334 ◽  
Author(s):  
Sizhou Sun ◽  
Lisheng Wei ◽  
Jie Xu ◽  
Zhenni Jin
Keyword(s):  
Feature Selection ◽  
Wind Speed ◽  
Wind Farms ◽  
Frequency Component ◽  
Ensemble Empirical Mode Decomposition ◽  
Two Stage ◽  
Wind Speed Forecasting ◽  
Wind Speed Data ◽  
Wind Speed Prediction ◽  
Mode Decomposition

Accurate wind speed prediction plays a crucial role on the routine operational management of wind farms. However, the irregular characteristics of wind speed time series makes it hard to predict accurately. This study develops a novel forecasting strategy for multi-step wind speed forecasting (WSF) and illustrates its effectiveness. During the WSF process, a two-stage signal decomposition method combining ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) is exploited to decompose the empirical wind speed data. The EEMD algorithm is firstly employed to disassemble wind speed data into several intrinsic mode function (IMFs) and one residual (Res). The highest frequency component, IMF1, obtained by EEMD is further disassembled into different modes by the VMD algorithm. Then, feature selection is applied to eliminate the illusive components in the input-matrix predetermined by partial autocorrelation function (PACF) and the parameters in the proposed wavelet neural network (WNN) model are optimized for improving the forecasting performance, which are realized by hybrid backtracking search optimization algorithm (HBSA) integrating binary-valued BSA (BBSA) with real-valued BSA (RBSA), simultaneously. Combinations of Morlet function and Mexican hat function by weighted coefficient are constructed as activation functions for WNN, namely DAWNN, to enhance its regression performance. In the end, the final WSF values are obtained by assembling the prediction results of each decomposed components. Two sets of actual wind speed data are applied to evaluate and analyze the proposed forecasting strategy. Forecasting results, comparisons, and analysis illustrate that the proposed EEMD/VMD-HSBA-DAWNN is an effective model when employed in multi-step WSF.

scholarly journals A Statistical Analysis and Datamining Approach for Wind Speed Predication

2014 ◽  
Vol 14 (2) ◽  
pp. 5464-5478
Author(s):  
Mahesh K ◽  
Dr M V Vijayakumar ◽  
Gangadharaiah. Y.H .
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Farms ◽  
Predictive Performance ◽  
Power Industry ◽  
New Paradigm ◽  
Prolog Program ◽  
Wind Speed Data ◽  
Wind Power Industry ◽  
Intelligent Technology

The wind power industry has seen an unprecedented growth in last few years. The surge in orders for wind turbines has resulted in a producers market. This market imbalance, the relative immaturity of the wind industry, and rapid developments in data processing technology have created an opportunity to improve the performance of wind farms and change misconceptions surrounding their operations. This research offers a new paradigm for the wind power industry, data-driven modeling. Each wind Mast generates extensive data for many parameters, registered as frequently as every minute. As the predictive performance approach is novel to wind industry, it is essential to establish a viable research road map. This paper proposes a data-mining-based methodology for long term wind forecasting (ANN), which is suitable to deal with large real databases. The paper includes a case study based on a real database of five years of wind speed data for a site and discusses results of wind power density was determined by using the Weibull and Rayleigh probability density functions. Wind speed predicted using wind speed data with Datamining methodology using intelligent technology as Artificial Neural Networks (ANN) and a PROLOG program designed to calculate the monthly mean wind speed.

Long-term effects of wakes from offshore wind farms on the wind conditions at FINO1

2020 ◽  
Author(s):  
Marcos Ortensi ◽  
Richard Fruehmann ◽  
Thomas Neumann
Keyword(s):  
Wind Speed ◽  
Turbulence Intensity ◽  
Wind Farms ◽  
Offshore Wind ◽  
Wind Flow ◽  
Long Term Effects ◽  
Offshore Wind Farms ◽  
Clear Effect ◽  
Research Platform

<p>Investigation on how the wind conditions at the FINO1 research platform have changed through the construction of new wind farms in the vicinity. The long measurement recorded at FINO1 opens the opportunity to analyze how the progressive construction of wind farms influences the downwind wind conditions over a range of distances. In previous publications it has been shown that the wakes from the nearby wind farms Alpha Ventus, Borkum Riffgrund 1 and Trianel Windpark Borkum I have a clear effect on the wind flow, causing a reduction in wind speed and an increase in turbulence intensity.</p>

Regionalization of wind-speed data to analyse tree-line wind conditions in the eastern Andes of southern Ecuador

Erdkunde ◽  
2015 ◽  
Vol 69 (1) ◽  
pp. 3-19 ◽  
Author(s):  
Julia Wagemann ◽  
Boris Thies ◽  
Rütger Rollenbeck ◽  
Thorsten Peters ◽  
Jörg Bendix
Keyword(s):  
Wind Speed ◽  
Tree Line ◽  
Wind Speed Data ◽  
Southern Ecuador

scholarly journals Evaluating the potential of offshore wind energy in the Gulf of Oman using the MENA-CORDEX wind speed data simulations

2021 ◽  
Vol 15 (1) ◽  
pp. 613-626
Author(s):  
Shahab S. Band ◽  
Sayed M. Bateni ◽  
Mansour Almazroui ◽  
Shahin Sajjadi ◽  
Kwok-wing Chau ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Offshore Wind ◽  
Offshore Wind Energy ◽  
Gulf Of Oman ◽  
Wind Speed Data
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