Analysis Of Wind Data Research Articles

Simulation of wind-diesel hybrid power system for a rural community in Nigeria

This paper presents a Wind-and-Diesel Hybrid power system to be sited at Abonnema, a community located in the suburb of Port Harcourt, Rivers State of Nigeria (latitude 4.71N, longitude 6.79E).This was achieved through five phases of work: a wind resource assessment was undertaken using Weibull probability distribution; an optimal wind turbine blade was designed applying the tip-loss model approach; the hybrid system was sized; Simulation using MATLAB® programme were performed on the hybrid power system; and an economic analysis was conducted on the system adopting the life cycle cost method. The results obtained on the wind data analysis showed that even at a high hub height, the wind speed of Port Harcourt zone is low. The simulation results obtained shows that wind-and-diesel hybrid power system with battery storage can be operated suitably to obtain a more reliable power supply as well as improve fuel savings. Economic analysis conducted shows the positive benefits of implementing the hybrid wind-and-diesel power system rather than continuing with the existing diesel power plant over the project life cycle of 20 years.Keywords: Wind power, diesel generator, hybrid power, design and simulation of power system.

Analysis of Wind Data and Assessment of Wind Energy Potential in Lamhiriz Village, Morocco

Morocco has pursued an ambitious policy aimed at boosting renewable energies in the country. On this basis, we have examined the possibility of using wind turbines to generate electricity in Lamhiriz village in the south of Morocco, known for its strong winds. The wind speeds were statistically studied using the data recorded over one-year period collected from the mast which contains a set of measuring instruments at different altitudes. Based on several studies, we selected Weibull distribution to model the wind speeds, and we used the empirical method to estimate the scale and the shape parameters. Thereafter, a detailed analysis of seasonal and annual wind direction was presented. From the obtained results, the available energy density in this area is assessed by using WAsP software. Then, six commercial wind turbines of various sizes between 300 and 1300 kW were technically evaluated for electricity generation in Lamhiriz village by calculating their capacity factors and annual energy outputs. The results showed that the site under study is an adequate location for any of the adopted wind turbine models, where the annual energy output ranges from 1.44 to 6.75 GW h. Therefore, we can conclude that Lamhiriz have an attractive economical potential for developing large wind turbine farms.

Performance Evaluation of Wind Turbines on the basis of Power Generation

Wind energy is one of the promising solution for future demand and need of energy. Main application of wind energy is in electricity sector. India is having potential for wind installation which is not yet fully explored. This paper compares the results from test rig small scale wind turbine and scale up data for different sizes of turbine as power from small size turbine was very low. A mathematical formulation for wind power is developed for finding total power from the available wind at certain speed. Three factors considered for analysis of three responses. Factors are Wind Speed, Rotor diameter and Swept area for getting response for wind power, torque and wind power density. For different wind speed and different rotor diameter wind power, torque and wind power density calculations done. Response surface method is used to design the experiments and ANOVA (Analysis of Variance) is used to analyze that experiment results are significant or not. Wind data analysis shows that wind speed gives a fairly good response in the range of wind speed 8m/s to 12m/s in practical grounds.

Wind Direction Measurements on Moored Coastal Buoys

AbstractWind direction measurements from anchored floating objects (buoys) are disturbed by the movement of an agitated sea surface. This article describes corrected wind direction measurements that account for a rotated and tilted anemometer, in which the rotations around the Euler angles “roll,” “pitch,” and “yaw” are simultaneously measured on coastal buoy “Vida” in Slovenian waters of the Gulf of Trieste. The wind data analysis is composed of a short-term analysis during a 4-h summer storm and a long-term analysis of wind direction for an 8-yr period. In the short-term analysis, the time evolution of the spectral composition of the anemometer’s translation and wind speeds revealed the dominance of 3–4-s oscillations that are mostly caused by waves. Anemometer velocity may account for up to 5% of apparent wind speed. In the long-term analysis, wind direction measurements were validated against those from two other stations in the Gulf of Trieste, as well as from the Acqua Alta tower in front of the Venice lagoon; all three are located in Italian waters. Short-term analysis of a summer storm and long-term measurements from four stations show that correcting the wind direction with the buoy’s orientation accounts for about 10°–12° of the error in wind direction.

Wind Data Analysis of Coastal Region of Balochistan (Pakistan) by Weibull and Rayleigh Method

Wind Data Analysis of Coastal Region of Balochistan (Pakistan) by Weibull and Rayleigh Method

THE EVALUATING OF WIND ENERGY POTENTIAL OF DİYARBAKIR USING WEIBUL AND RAYLEIGH DISTRIBUTION

In comparison to fossil fuel that pollutes the lower layer of the atmosphere, wind energy is an alternative clean source of energy. Wind speed is the parameter with primary importance in designing and studying the wind energy conversion systems. In this study, the statistical analysis of the parameters of wind power density and wind speed distribution was investigated using the wind speed data of Diyarbakır province between the years 2014 and 2018 that were hourly measured by the General Directorate of Meteorology. Wind data are used to derive probability distributions, and their distributional parameters are determined. Two probability density functions are suited to the measured probability distributions on a yearly basis. The Weibull and the Rayleigh models are used to analyze the wind energy potential of the location. This modeling process was evaluated according to R 2 , RMSE and c 2 parameters. In conclusion, the values obtained by the Weibull model gave better results than the values obtained by the Rayleigh model in the wind data analysis of Diyarbakır province.

Wind Data Analysis, Annual Resource Estimation And Comparison With Measured Annual Energy Yield at the University Wind Turbine

Evaluation of the power potential of a particular type of wind turbine at a specific site is necessary for economic decisions. Therefore, the information of a wind turbine and that of a site have to be measured or predicted and then combined with the power curve of a wind turbine. The main objective of this research was to predict the power potential of the existing small wind turbine with a diameter of 3m and the wind turbine site at the University of Siegen and compare with the annual energy calculated from the measured one year of wind and turbine data. Techniques for prediction of the wind speed distribution of a site were determined and modeled. The power curve of the wind turbine was modeled from data recorded by applying a technique from the novel methods for modelling the power curve. In this research, artificial neural network, Weibull and Rayleigh are the techniques modeled to predict wind speed distribution at the wind turbine site. Rayleigh and Weibull were chosen since the two models depict a better wind speed distribution and require the mean and the standard deviation of the wind speed at the wind turbine site. A neural network trained with the backward propagation levernberg-Marquardt algorithm was applied to predict the wind speed and power potential of the wind turbine site. A comparison between Weibull, Rayleigh and the Levernberg-Marquardt trained neural network wind speed was made. The power curve of the wind turbine was successfully evaluated from wind data and wind turbine data recorded. The results indicate that the annual mean wind speed of the region is 2.54 (m/s) and about 20% of the wind availability was blowing from the west. The annual energy yield predicted from the trained neural network was 372 (kWh) closer to that determined from measured wind speed 360 (kWh) than that determined from Weibull and Rayleigh 337 and 233 (kWh) respectively. The three prediction models are applicable in any region to predict the annual energy of a particular wind turbine site with minimal data available.

Wind Data Analysis, Annual Resource Estimation And Comparison With Measured Annual Energy Yield at the University Wind Turbine

Evaluation of the power potential of a particular type of wind turbine at a specific site is necessary for economic decisions. Therefore, the information of a wind turbine and that of a site have to be measured or predicted and then combined with the power curve of a wind turbine. The main objective of this research was to predict the power potential of the existing small wind turbine with a diameter of 3m and the wind turbine site at the University of Siegen and compare with the annual energy calculated from the measured one year of wind and turbine data. Techniques for prediction of the wind speed distribution of a site were determined and modeled. The power curve of the wind turbine was modeled from data recorded by applying a technique from the novel methods for modelling the power curve. In this research, artificial neural network, Weibull and Rayleigh are the techniques modeled to predict wind speed distribution at the wind turbine site. Rayleigh and Weibull were chosen since the two models depict a better wind speed distribution and require the mean and the standard deviation of the wind speed at the wind turbine site. A neural network trained with the backward propagation levernberg-Marquardt algorithm was applied to predict the wind speed and power potential of the wind turbine site. A comparison between Weibull, Rayleigh and the Levernberg-Marquardt trained neural network wind speed was made. The power curve of the wind turbine was successfully evaluated from wind data and wind turbine data recorded. The results indicate that the annual mean wind speed of the region is 2.54 (m/s) and about 20% of the wind availability was blowing from the west. The annual energy yield predicted from the trained neural network was 372 (kWh) closer to that determined from measured wind speed 360 (kWh) than that determined from Weibull and Rayleigh 337 and 233 (kWh) respectively. The three prediction models are applicable in any region to predict the annual energy of a particular wind turbine site with minimal data available.

Role of Averaging in Statistical Analysis of Solar Wind Data from the DSCOVR Spacecraft for the First Year of Operation

The results from a statistical analysis of solar wind data obtained near the Earth’s orbit by the DSCOVR spacecraft for the period of one year starting August 2016 are discussed. The distribution of the solar wind temperature T for this period was bimodal and exhibited two maxima at 15 × 103 and 190 × 103 K (with a standard deviation of the same order of magnitude, respectively). This distribution can be represented as the sum of two lognormal distributions. The first peak of the T distribution is caused by the slow-cold wind component, the distributions of which over speed V and density n (varying in wide ranges) are also close to lognormal. This component covers slightly more than a quarter of the entire time period. A fast hot wind (the distribution maximum is at 310 × 103 K) can be distinguished from the remainder; this component covers slightly less than a quarter of the period. Its T, V, and n distributions are also close to lognormal. The rest the wind, nearly half, exhibits a complex “jagged” distribution over speed and a double-peak distribution over density. The conclusion is that data averaging is of key importance in the classification of solar wind at the quantitative level. This is shown by the results of data averaging over a minute, hour, day, and week.

PRIORITAS ARAH PENEMPATAN TITIK PENGAMATAN KECEPATAN VERTIKAL DI KALIMANTAN MENGGUNAKAN ANALISIS ANISOTROPI

Keberadaan hutan sebagai paru-paru dunia berperan penting dalam menghasilkan gas oksigen. Pulau Kalimantan merupakan salah satu paru-paru dunia karena luas hutannya yang mencapai 40,8 juta hektar. Namun, pada saat ini kualitas dan kuantitas hutan di Pulau Kalimantan mengalami penurunan drastis akibat adanya deforestasi dan kebakaran hutan. Kebakaran hutan di Pulau Kalimantan menjadi sorotan dunia karena persebaran asapnya menyebabkan polusi udara di berbagai wilayah Indonesia. Dampak dari persebaran asap kebakaran hutan dipengaruhi oleh kecepatan angin vertikal pada daerah tersebut. Penelitian ini bertujuan untuk menambah bahan pertimbangan dalam menentukan titik pengamatan kecepatan vertikal angin di Pulau Kalimantan, sehingga dapat diketahui prioritas sebaran penempatan titik pengamatan yang dapat digunakan sebagai upaya mitigasi persebaran asap kebakaran hutan. Data kecepatan angin vertikal Pulau Kalimantan pada bulan Juni sampai Oktober mulai tahun 2008 – 2017 disajikan dalam bentuk spasial. Analisis data angin tersebut menggunakan metode variogram permukaan dengan piranti lunak SAGA. Berdasarkan analisis anisotropi, diperoleh hasil bahwa pengamatan kecepatan vertikal angin di Pulau Kalimantan cenderung mempunyai prioritas arah Timur-Barat pada bulan Juni sampai Agustus, prioritas arah Tenggara-Barat Laut pada bulan September, dan prioritas arah Timur Laut-Barat Daya pada Bulan Oktober. Analisis dan pemodelan kecepatan angin vertikal pada Pulau Kalimantan secara umum mendapatkan pola dominan prioritas arah Timur-Barat, sehingga prioritas penempatan titik pengamatan kecepatan vertikal pada Pulau Kalimantan efektif jika membentuk pola oval yang memanjang dari Utara ke Selatan.

Adaptive test for ergodic diffusions plus noise

We propose some parametric tests for an ergodic diffusion-plus-noise model, which is a version of state-space modelling in statistics for stochastic diffusion equations. The test statistics are classified into three types: likelihood-ratio-type test; Wald-type; and Rao-type. All the test statistics are constructed with quasi-likelihood-functions for a local mean sequence of noised observation. We also simulate their behaviour for several practical hypothesis tests and check the convergence in law of test statistics under null hypotheses and consistency of the test under alternative ones. We apply the method for real data analysis of wind data and examine some sets of the hypotheses mainly with respect to the structure of the diffusion coefficient.

Ambient infrasound noise in urban environments

Associated with the emerging use of infrasound at tactical ranges (less than 50 km in complicated terrain), there is an increased need to deploy infrasound arrays in urban environments near sources of interest in order to record small signals. Initial urban deployment research demonstrated successful monitoring of a single source within 1 km utilizing rooftop infrasound arrays. In order to understand the applicability of this technique for a broader range of sources, the impact of the urban array design on noise reduction and detection of signals of interest must be assessed. These source signals, recorded within an urban scenario, are embedded in a complicated ambient noise environment with multiple coherent clutter sources and an overall decrease in the signal-to-noise ratio. Understanding these ambient acoustic field characteristics requires deconvolving observed coherent signals from time-varying atmospheric effects for different levels of urbanization. This paper presents an initial approach to instrumenting the urban environment with consideration of wind filter effects and data analysis to estimate total ambient acoustic field for a subset of urban zones. This work then provides a definition of urbanization that can be related to the infrasound wavefield. Permission to publish was granted by the Director, Geotechnical and Structures Laboratory.

STATISTICAL ANALYSIS OF WIND SPEED AND EVALUATION OF WIND POWER DENSITY FOR COLABA, MUMBAI

The fuel energy resources around the globe are declining at a faster rate. The greenhouse gases emitted in the process of harnessing energy from the non-renewable resources such as coal, fossil-fuel and radioactive substances cause the global warming and there is a danger of climate change. All these factors compel the researchers to explore renewable sources for power generation. Wind is a renewable resource, which is available naturally and is practically inexhaustible. In this study, the hourly wind data collected between the years 2013 to 2015 is evaluated. The wind data at the height of 11 m was obtained from a wind station at the Indian Meteorological Department, Colaba (Mumbai, India). The highest monthly wind speed measured was 5.11 m/s and the yearly average was found to be 2.72 m/s. various distribution functions were statistically analyzed and it was observed that the Two-parameter Weibull distribution gives the best fit to the actual data. The mean wind power density was 73.013 W/m2. The maximum wind speeds can be observed between the months of June and September. The wind data analysis shows that if a domestic wind turbine is installed at this site, it will generate enough energy for power saving. This paper aims to promote the growth of small wind power plant in a developing country like India.

Assessment of fresh water wind resources on Lake Erie

Wind farms are better been built at locations with higher wind resource potentials. As the appropriate locations become fewer and fewer to build onshore wind farms, significant attention has been drawn to the wind energy industry to build offshore wind farms. The terrain effect has fewer effects offshore than onshore since the sea level is flat and no artificial buildings are built there. The coastal line of the Great Lakes is one of those areas that not only has great wind energy potential but is also near the high population coastal cities which is short of the land surface. This article makes the detailed statistical analysis of 1-year offshore wind data in Lake Erie from a Light Detection and Ranging system placed on a water intake crib 4 miles away from near the coast of Cleveland. For comparison purpose, a nearby onshore wind monitoring station’s data have also been analyzed to study the wind and power characteristics. Specifically, the statistical analysis of the data includes Weibull shape and scale factors, the monthly average of the wind speed, turbulence intensity, and wind power density. In addition, two site-matching commercial wind turbines with 50 (Vestas® 39) and 80 m (Vestas® V90) hub heights have been chosen to estimate the 1-year energy output. The result shows great preponderances of building offshore wind farms than building onshore wind farms. This study gives guidance to the cost-benefit analysis to build the offshore wind farms in Lake Erie.

Performance of Global Climate Model (GCMs) for Wind Data Analysis

The surface wind speed is an important climate variable for study of ocean wave energy and coastal erosion. The wind speed and wave height variations are caused by global warming. In the future, climate change impacts on changes of direction and wind speed which affect on wave height and wave period. The global climate model (GCMs) were developed by various institutions so each GCM has different GCM output. Then, the aim of this study is to evaluation the performance of GCMs for wind speed analysis in the area of Gulf of Thailand and Andaman Sea. In this study, the daily wind speed data was analyzed with a total of 15 GCMs and daily wind speed data of NCEP-NCAR was used as observation data to compare with wind speed data from GCMs over the period 1986-2005 (20 years). Moreover, the wind speed data was evaluated by efficiency coefficient which are root mean square error (RMSE) and mean absolute error (MAE). It was found tht MRI-CGCM3, GFDL-ESM2M, IPSL-CM5A-LR, and IPSL-CM5A-MR are consistent with the most of observation data from NCEP-NCAR.

Wind Data Collection and Analysis of Topographical Features along a Highway for Traffic Safety Assessment Based on Mobile Mapping Technology

Strong crosswind is one of the main factors that may cause traffic collisions. Because the wind velocity is influenced by the roadside environment and surrounding terrain, its distribution varies in both the temporal and spatial domains. Therefore, locations with a high probability of strong crosswind should be identified and safety measures should be implemented at these sites. However, geographical data of continuous winds along a highway cannot easily be obtained using existing technology. This prompted the development of a method for geo-location positioning and terrain analysis with elevation data in ArcGIS in combination with mobile mapping technology. The method was applied in a field test conducted on three different highways in China to identify places at which stronger crosswinds occur. The results showed that the proposed method can successfully obtain site-specific wind and crosswind velocity data. It was found that strong winds along the tested highways usually occur at a saddleback or at the border of a riverbank and river, whereas crosswinds are relatively stronger in sections connecting a bridge and tunnel, a bridgehead, or a cross-sea bridge. This information will be useful for future highway projects and traffic safety assessment.

Analysis of Wind Data, Calculation of Energy Yield Potential, and Micrositing Application with WAsP

The parameters required for building a wind power plant have been calculated using the fuzzy logic method by means of Wind Atlas Analysis and Application Program (WAsP) in this study. Overall objectives of the program include analysis of raw data, evaluation of wind and climate, construction of a wind atlas, and estimation of wind power potential. With the analysis performed in the application, the average wind velocity, average power density, energy potential from micrositing, capacity factor, unit cost price, and period of redemption have been calculated, which are needed by the project developer during the decision-making stage and intended to be used as the input unit in the fuzzy logic-based system designed. It is aimed at processing the parameters calculated by the designed fuzzy logic-based decision-making system at the rule base and generating a compatibility factor that will allow for making the final decision in building wind power plants.

Airborne Doppler Wind Lidar Observations of the Tropical Cyclone Boundary Layer

This study presents a verification and an analysis of wind profile data collected during Tropical Storm Erika (2015) by a Doppler Wind Lidar (DWL) instrument aboard a P3 Hurricane Hunter aircraft of the National Oceanic and Atmospheric Administration (NOAA). DWL-measured winds are compared to those from nearly collocated GPS dropsondes, and show good agreement in terms of both the wind magnitude and asymmetric distribution of the wind field. A comparison of the DWL-measured wind speeds versus dropsonde-measured wind speeds yields a reasonably good correlation (r2 = 0.95), with a root mean square error (RMSE) of 1.58 m s−1 and a bias of −0.023 m s−1. Our analysis shows that the DWL complements the existing P3 Doppler radar, in that it collects wind data in rain-free and low-rain regions where Doppler radar is limited for wind observations. The DWL observations also complement dropsonde measurements by significantly enlarging the sampling size and spatial coverage of the boundary layer winds. An analysis of the DWL wind data shows that the boundary layer of Erika was much deeper than that of a typical hurricane-strength storm. Streamline and vorticity analyses based on DWL wind observations explain why Erika maintained intensity in a sheared environment. This study suggests that DWL wind data are valuable for real-time intensity forecasts, basic understanding of the boundary layer structure and dynamics, and offshore wind energy applications under tropical cyclone conditions.

Statistical Analysis Of Wind Speed Distribution Of Turkey As Regional

In this study, the statistical analysis of wind power density and wind speed distribution parameters of the selected cities from seven region of Turkey was investigated using the hourly wind speed data measured by the Turkish State Meteorological Service between 2005 and 2014. The Weibull and Rayleigh distributions were used for modeling and the success of this modeling process was evaluated according to the parameters of R2, RMSE and c2. The Weibull parameters and the Rayleigh parameters were estimated analytically, and the mean wind speed and energy potential were determined based on these parameters. At the Weibull distribution, the lowest mean wind speed and power density was obtained as 1.73 m/s and 5.78 W/m2 in Adıyaman, respectively. The highest mean speed and power density was determined as 2.95 m/s and 33.32 W/m2 in Sinop. At the Rayleigh distribution, the lowest and the highest mean speed and the power density was obtained as 1.72 m/s and 5.63 W/m2 in Adıyaman, 3.06 m /s and 33.44 W/m2 in Sinop, respectively. Generally, the highest mean wind speed and power density values were determined in Sinop, and the lowest mean wind speed and power density values in Adıyaman. According to statistical criteria in wind data analysis of Turkey, the Weibull distribution was better than the Rayleigh distribution.

Statistical analysis of wind characteristics using Weibull and Rayleigh distributions in Deokjeok-do Island – Incheon, South Korea

Abstract Wind data measured at the site location called Deokjeok-do Island – Incheon, South Korea, has been analyzed by the two-parameter Weibull probability density function in order to estimate the wind energy potential. Wind data collected at 10 m height, including wind speed and wind direction, recorded for seventeen years (2000–2016) with one minute time interval. Detailed analysis of wind data has been carried out using different statistical parameters in order to understand the wind characteristics and to predict the wind behavior during different years, seasons, months, days and every hour. Throughout the analysis of wind characteristics, it is found that during all the seasons most of the winds either come from east-south (ES) or from south-west (SW) with the maximum magnitude of below 8 m/s but mostly between 2 and 3 m/s. Main wind directions are due to the climate characteristics of Korea and the geographical features of the studied region. Weibull shape and scale parameters for the region have also been analyzed for all years, months and seasons. Based on the statistical analysis of wind conditions presented here, the results of current study can be used to make a sustainable energy plan for Deokjeok-do.