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.

Analyzing wind speed data and wind power density of Tetouan city in Morocco by adjustment to Weibull and Rayleigh distribution functions

2017 ◽  
Vol 41 (3) ◽  
pp. 174-184 ◽  
Author(s):  
Mohamed Hatim Ouahabi ◽  
Farid Benabdelouahab ◽  
Abdellatif Khamlichi
Keyword(s):  
Wind Speed ◽  
Measurement Data ◽  
Rayleigh Distribution ◽  
Distribution Functions ◽  
Statistical Distributions ◽  
Energy Potential ◽  
Distribution Models ◽  
Wind Speed Data ◽  
Resource Characteristics ◽  
Wind Resource

Several statistical distributions have been considered to model wind speed data. However, Weibull and Rayleigh statistical distributions are the most widely used methods for analyzing wind speed measurements and determining wind energy potential. In this work, these statistical distributions were applied in order to evaluate the wind resources in the northern Moroccan city of Tetouan. Adjustment of wind measurement data was performed. Then, the obtained results were compared with the provided wind data to test their accuracy based on common statistical indicators for performance. It was found that the Weibull and Rayleigh distribution models provide adequate description of the frequencies of actual wind records in Tetouan. They can be advantageously used to assess wind resource characteristics in this region.

scholarly journals Evaluation of the Technical Wind Energy Potential of Kisii Region Based on the Weibull and Rayleigh Distribution Models

2018 ◽  
Author(s):  
Laban N. Ongaki ◽  
Christopher M. Maghanga ◽  
Joash Kerongo
Keyword(s):  
Wind Speed ◽  
Rayleigh Distribution ◽  
Series Data ◽  
Energy Potential ◽  
Distribution Models ◽  
Wind Speeds ◽  
Average Wind ◽  
Mean Wind Speed ◽  
Wind Energy Potential

The research sought to investigate the long term characteristics of wind in the Kisii region (elevation 1710m above sea level, 0.68oS, 34.79o E). Wind speeds were analyzed and characterized on short term (per month for a year) and then simulated for long term (ten years) measured hourly series data of daily wind speeds at a height of 10m. The analysis included daily wind data which was grouped into discrete data and then calculated to represent; the mean wind speed, diurnal variations, daily variations as well as the monthly variations. The wind speed frequency distribution at the height 10 m was found to be 2.9ms-1 with a standard deviation of 1.5. Based on the two month’s data that was extracted from the AcuRite 01024 Wireless Weather Stations with 5-in-1 Weather Sensor experiments set at three sites in the region, averages of wind speeds at hub heights of 10m and 13m were calculated and found to be 1.7m/s, 2.0m/s for Ikobe station, 2.4m/s, 2.8m/s for Kisii University stations, and 1.3m/s, 1.6m/s for Nyamecheo station respectively. Then extrapolation was done to determine average wind speeds at heights (20m, 30m, 50m, and 70m) which were found to be 85.55W/m2, 181.75W/m2, 470.4W/m2 and 879.9W/m2 respectively. The wind speed data was used statistically to model a Weibull probability density function and used to determine the power density for Kisii region.

scholarly journals Wind speed modeling based on measurement data to predict future wind speed with modified Rayleigh model

2021 ◽  
Vol 12 (3) ◽  
pp. 1823
Author(s):  
Suwarno Suwarno ◽  
Rohana Rohana
Keyword(s):  
Wind Speed ◽  
Measurement Data ◽  
Rayleigh Distribution ◽  
Scale Factor ◽  
Percentage Error ◽  
Monthly Data ◽  
Wind Speeds ◽  
Wind Characteristics ◽  
One Year ◽  
The Many

The development of modeling wind speed plays a very important in helping to obtain the actual wind speed data for the benefit of the power plant planning in the future. The wind speed in this paper is obtained from a PCE-FWS 20 type measuring instrument with a duration of 30 minutes which is accumulated into monthly data for one year (2019). Despite the many wind speed modeling that has been done by researchers. Modeling wind speeds proposed in this study were obtained from the modified Rayleigh distribution. In this study, the Rayleigh scale factor (<em>C<sub>r</sub></em>) and modified Rayleigh scale factor (<em>C<sub>m</sub></em>) were calculated. The observed wind speed is compared with the predicted wind characteristics. The data fit test used correlation coefficient (R<sup>2</sup>), root means square error (RMSE), and mean absolute percentage error (MAPE). The results of the proposed modified Rayleigh model provide very good results for users.

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 A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1596 ◽  
Author(s):  
Xin Zhao ◽  
Haikun Wei ◽  
Chenxi Li ◽  
Kanjian Zhang
Keyword(s):  
Kalman Filter ◽  
Wind Speed ◽  
State Space ◽  
State Space Model ◽  
Hybrid Approach ◽  
Wind Farms ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Space Model ◽  
Wind Speeds

The ability to predict wind speeds is very important for the security and stability of wind farms and power system operations. Wind speeds typically vary slowly over time, which makes them difficult to forecast. In this study, a hybrid nonlinear estimation approach combining Gaussian process (GP) and unscented Kalman filter (UKF) is proposed to predict dynamic changes of wind speed and improve forecasting accuracy. The proposed approach can provide both point and interval predictions for wind speed. Firstly, the GP method is established as the nonlinear transition function of a state space model, and the covariance obtained from the GP predictive model is used as the process noise. Secondly, UKF is used to solve the state space model and update the initial prediction of short-term wind speed. The proposed hybrid approach can adjust dynamically in conjunction with the distribution changes. In order to evaluate the performance of the proposed hybrid approach, the persistence model, GP model, autoregressive (AR) model, and AR integrated with Kalman filter (KF) model are used to predict the results for comparison. Taking two wind farms in China and the National Renewable Energy Laboratory (NREL) database as the experimental data, the results show that the proposed hybrid approach is suitable for wind speed predictions, and that it can increase forecasting accuracy.

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 Supplying Baseload Power and Reducing Transmission Requirements by Interconnecting Wind Farms

2007 ◽  
Vol 46 (11) ◽  
pp. 1701-1717 ◽  
Author(s):  
Cristina L. Archer ◽  
Mark Z. Jacobson
Keyword(s):  
Wind Speed ◽  
Electric Power ◽  
Wind Power ◽  
Electric Energy ◽  
Wind Farms ◽  
Long Distance ◽  
Annual Average ◽  
Transmission Grid ◽  
Wind Speeds ◽  
Average Wind

Abstract Wind is the world’s fastest growing electric energy source. Because it is intermittent, though, wind is not used to supply baseload electric power today. Interconnecting wind farms through the transmission grid is a simple and effective way of reducing deliverable wind power swings caused by wind intermittency. As more farms are interconnected in an array, wind speed correlation among sites decreases and so does the probability that all sites experience the same wind regime at the same time. The array consequently behaves more and more similarly to a single farm with steady wind speed and thus steady deliverable wind power. In this study, benefits of interconnecting wind farms were evaluated for 19 sites, located in the midwestern United States, with annual average wind speeds at 80 m above ground, the hub height of modern wind turbines, greater than 6.9 m s−1 (class 3 or greater). It was found that an average of 33% and a maximum of 47% of yearly averaged wind power from interconnected farms can be used as reliable, baseload electric power. Equally significant, interconnecting multiple wind farms to a common point and then connecting that point to a far-away city can allow the long-distance portion of transmission capacity to be reduced, for example, by 20% with only a 1.6% loss of energy. Although most parameters, such as intermittency, improved less than linearly as the number of interconnected sites increased, no saturation of the benefits was found. Thus, the benefits of interconnection continue to increase with more and more interconnected sites.

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.

scholarly journals Performance evaluation of Weibull function for wind data analysis in two selected locations in North-Western, Nigeria

2018 ◽  
Vol 6 (1) ◽  
pp. 18
Author(s):  
Boluwaji Olomiyesan
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Predictive Ability ◽  
Maximum Energy ◽  
Wind Data ◽  
Weibull Function ◽  
Wind Speed Data ◽  
Wind Speeds ◽  
The North ◽  
North Western

In this study, the predictive ability of two-parameter Weibull distribution function in analyzing wind speed data was assessed in two selected sites with different mean wind speeds in the North-Western region of Nigeria. Twenty-two years wind speed data spanning from 1984 to 2005 was used in the analysis. The data were obtained from the Nigerian Meteorological Agency (NIMET) in Lagos. The results of the analysis show that Weibull function is suitable for analyzing measured wind speed data and in predicting the wind-power density in both locations and that Weibull function is not discriminative between locations with high and low mean wind speeds in analyzing wind data. The annual mean wind speeds for the two sites (Sokoto and Yelwa) are 7.99 ms-1 and 2.59 ms-1 respectively, while the annual values of the most probable wind speed and the maximum, energy-carrying wind speeds are respectively:3.52 and 4.34 ms-1 for Yelwa and 8.33 and 9.02 ms-1 for Sokoto. The estimated annual wind power densities for Yelwa and Sokoto are respectively 36.91 and 359.96 Wm-2. Therefore, Sokoto has a better prospect for wind power generation.

scholarly journals Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2158 ◽  
Author(s):  
Mekalathur B Hemanth Kumar ◽  
Saravanan Balasubramaniyan ◽  
Sanjeevikumar Padmanaban ◽  
Jens Bo Holm-Nielsen
Keyword(s):  
Wind Speed ◽  
Frequency Distribution ◽  
Wind Direction ◽  
Optimization Method ◽  
Wind Data ◽  
Gray Wolf ◽  
Energy Potential ◽  
Weibull Parameters ◽  
Wind Speeds ◽  
Wind Potential

In this paper the multiverse optimization (MVO) was used for estimating Weibull parameters. These parameters were further used to analyze the wind data available at a particular location in the Tirumala region in India. An effort had been made to study the wind potential in this region (13°41′30.4″ N 79°21′34.4″ E) using the Weibull parameters. The wind data had been measured at this site for a period of six years from January 2012 to December 2017. The analysis was performed at two different hub heights of 10 m and 65 m. The frequency distribution of wind speed, wind direction and mean wind speeds were calculated for this region. To compare the performance of the MVO, gray wolf optimizer (GWO), moth flame optimization (MFO), particle swarm optimization (PSO) and other numerical methods were considered. From this study, the performance had been analyzed and the best results were obtained by using the MVO with an error less than one. Along with the Weibull frequency distribution for the selected region, wind direction and wind speed were also provided. From the analysis, wind speed from 2 m/s to 10 m/s was present in sector 260–280° and wind from 0–4 m/s were present in sector 170–180° of the Tirumala region in India.

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