Annual Wind Speed Distributions Research Articles

Investigation of Seasonal and Annual Wind Speed Distribution of Tarnab Based on Weibull and Rayleigh Distribution Models

This study aims to statistically analyze wind speed data of Tarnab, Peshawar, for the period 2004-2023. The data was recorded at the Agriculture Research Institute, Tarnab, Peshawar. Two statistical models (two-parameter Weibull and Rayleigh distribution functions) were applied to find the distributions of wind speeds. For the estimation of shape and scale parameters of Weibull and Rayleigh, two methods were employed: the method of moments and the energy pattern factor. Three statistical tools (mean percentage error, mean absolute percentage error, and root mean square deviation) were applied to check the error percentage of both models. The results of the Weibull distribution were much closer to the observed data than those of the Rayleigh distribution. The average values of wind speeds tended to increase from winter to summer and vice versa. The highest recorded annual and seasonal wind speeds were 26.19 in/s and 41.57 in/s, respectively, while the lowest values were 7.11 in/s and 4.95 in/s, respectively. Thus, while ruling out the possibility of harnessing wind as a significant source of energy, the findings are still useful for the crops produced in the region.

A analytical study of wind speed distribution at Government first grade college premises in Hosadurga, India

Modelling of wind speed distribution (WSD) has become a necessary step in estimating the wind energy potential of a specific location. The statistical analysis of the potential for wind speed distribution at Government First Grade College [Lat. 13.79° N and Long. 76o28′ E] in Hosadurga city (Karnataka, India) is presented in this article. Over the course of a year, the wind speed at this location was recorded at the elevation of 60 m over ground. The annual WSD have been modelled using the scale parameter (c) and shape parameter (k) from the Weibull probability distribution (WPD). Weibull and normal approaches were used to compute the frequency distribution, which were found to be equivalent. Furthermore, the capacity factors (CFs) for commercially available wind energy conversion systems (WECS) have been evaluated to accomplish the efficient electrical power production in a given location.

Selection of an efficient wind turbine generator at Government science college premises Chitradurga, India

Wind speed modelling is very crucial in estimating the potential of wind energy for a particular site. In this paper, the wind energy potential at the premises of Government Science College area [Lat. 14°13’′ N and Long. 76°27′ E] in Chitradurga town (Karnataka State, India) is statistically analyzed for the year 2019 – 2020. The speed of wind has been assessed at the height of 60 m from the ground level at the site. The seasonal and annual wind speed distributions (WSD) have been modelled using Weibull probability distribution functions (WPDF) i.e., shape parameter, k and scale parameters, c. With the use of them, the wind power density (WPD) has been determined. The WPD estimated from Weibull and normal methods has been found to be comparable. Further, at a height of 60 m above the ground, the capacity factors (CPs) of various marketablly useable wind turbine generators have been estimated to achieve the optimal power generation for the chosen site.

Optimization of a Small Wind Power Plant for Annual Wind Speed Distribution

This article presents a method to adjust the elements of a small wind power plant to the wind speed characterized by the highest annual level of energy. Tests were carried out on the basis of annual wind distributions at three locations. The standard range of wind speeds was reduced to that resulting from the annual wind speed distributions in these locations. The construction of the generators and the method of their excitation were adapted to the characteristics of the turbines. The results obtained for the designed power plants were compared with those obtained for a power plant with a commercial turbine adapted to a wind speed of 10 mps. The generator structure and control method were optimized using a genetic algorithm in the MATLAB program (Mathworks, Natick, MA, USA); magnetostatic calculations were carried out using the FEMM program; the simulations were conducted using a proprietary simulation program. The simulation results were verified by measurement for a switched reluctance machine of the same voltage, power, and design. Finally, the yields of the designed generators in various locations were determined.

A new distribution for modeling wind speed characteristics and evaluating wind power potential in Xinjiang, China

ABSTRACT In this study, the authors conduct the research of the wind speed characteristics and the wind energy assessment based on the data at a height of 10 m in Xinjiang Uygur Autonomous Region which is located in the northwest part of China. The analysis shows that the wind is strong in the east-west direction, and the wind speed is concentrated in the range of 2 m/s to 9 m/s. Moreover, it has obvious periodicity and not large skewness, hence we need to find a model that can flexibly fit the wind speed distribution in the area. Then, we introduce a new flexible distribution for the first time, called Alpha logarithmic transformed Log-normal (ALTLN). Thirdly, the data are split and an analysis is made for the typically monthly, quarterly, and annual wind speed distribution and wind power density. The results show that the coefficient of determination (R2) is almost 99%, the root-mean-square error (RMSE) is less than 0.05, and most of the results pass the Kolmogorov–Smirnov (K-S) test which means the corresponding p values greater than 0.05 and the values are between 0.2 and 0.8. Finally, we come to the conclusions that the ALTLN distribution has good applicability to fit wind speed distribution and annual wind energy output reaches several thousands of gigawatt-hours, especially to Kashi. These results are helpful to develop the wind energy better for relevant departments.

On the inter-annual variability of wind energy generation – A case study from Germany

The intermittent and stochastic nature of the wind resource complicates constant electricity supply in countries with high wind energy share in the electricity mix. Therefore, the goal of this study was to quantify the inter-annual variability of wind energy generation on the national scale by estimating upper and lower limits of annual wind energy generation (WEG). A novel methodology was developed and is presented for Germany, where onshore wind energy already accounts for more than 15% of net electricity consumption. First, a comprehensive wind turbine data set was produced including all onshore wind turbines operating in 2017. Next, the wind speed-wind shear model (WSWS) was used to reconstruct the high spatial resolution (200 m × 200 m) annual wind speed distributions in the wind turbine hub height range 30–179 m above ground level in the period 1979–2017. By using wind turbine-specific power curves, the annual wind energy yield was calculated for each wind turbine. It was summed up for the entire country, yielding WEG. Then, 16 theoretical distributions were fitted to WEG. From the fitted distributions, long-term return values of WEG were calculated. In a 100-year period (probability 98%), WEG lies between 67 and 112 TWh/yr and the annual greenhouse gas mitigation potential varies between 45.6 and 76.3 Mio. tCO2-equiv. under current climate. The great WEG-range emphasizes the importance of considering upper and lower WEG-limits for ensuring constant electricity supply at the national scale.

A mathematical approach to minimizing the cost of energy for large utility wind turbines

With the aim of reducing green gas emission, wind turbine installations worldwide have grown rapidly in recent years. Wind energy itself is free, but has costs due to the wind turbine infrastructure and maintenance. The installation size of the wind turbine at a specific location is not only determined by the wind statistics at that location, but also by the turbine infrastructure and the maintenance cost. The payback time of the turbine is determined by the turbine cost of energy (COE). In this paper, a mathematical approach is proposed to minimize the turbine cost of energy based on wind statistics. Turbine annual energy production (AEP) is calculated based on turbine output power and annual wind speed distribution. A wind turbine cost model developed by U.S. National Renewable Energy Laboratory (NREL) is used for turbine cost analysis. The turbine cost of energy model includes the turbine rated power and the turbine rated wind speed. Finally a general guideline to minimize the turbine COE is presented. Three case studies are conducted to show the effectiveness of the proposed approach.

A long-term perspective of wind power output variability

The study of the wind power output variability comprises different time scales. Among these, low-frequency variations can substantially modify the performance of a wind power plant during its lifetime. In recent years, other temporal scales such as the short-term variability or the climatological conditions of wind and the corresponding generated power have been investigated in depth. However, the study of longer decadal and multidecadal variations is still in its early stages. In this work, the wind power output long-term variability is analysed for two locations in Spain, during the period 1871–2009. This is attained by computing the annual wind speed probability density functions derived from an ensemble of atmospheric sea level pressure data set through a statistical downscaling based in evolutionary algorithms. Results reveal significant trends and periodicities in multidecadal bands including 13, 25 and 46 years, as well as significant differences among both sites. The impact of the leading large-scale circulation patterns (NAO, EA, SCAND and AMO) on wind power output and its stationarity is analysed. Results on both locations show non-stationary significant and opposite seasonal couplings with these forcings. Finally, the long-term variability of the reconstructed Weibull parameters of the annual wind speed distributions is used to derive a linear model to estimate the annual wind power.

J056034 空力弾性解析による小形風車翼の確率論的疲労評価法の検討(〔J056-03〕再生可能エネルギー(風力発電)(3))

In this research, we aim to reveal, 1. The difference of fatigue load from aeroelastic model and SLM 2. The reasons of difference between aeroelastic model and SLM 3. The applicability of the idea that operation around Vdesign can represent the lifetime fatigue load which is the background of SLM For fatigue load design of small wind turbines, IEC61400-2 Ed.2[1] provides two fatigue load calculation methods; Simplified load model(SLM) and aeroelastic modeling. Although aeroelastic modeling includes more realistic effect, most of small wind turbines below 1kW in Japan are designed using SLM because of the complicated procedure of aeroelastic modeling. So, we aims to reveal how SLM is applicable for these “micro wind turbines” and how these turbines designed for SLM are safety or danger sided or how much the cost of energy increase or decrease using SLM instead of aeroelastic model. Moreover, we wish to establish more practical and theoretical load design method affordable for micro wind turbine manufacturers. In this research, fatigue load design is focused. Fatigue load estimation by the aeroelastic modeling was carried out for AURA1000 135W small wind turbine with 1m diameter In this research. The results were compared with the simplified load model (SLM). And the reasons of difference between two methods are discussed. An aeroelastic simulation code for 5 bladed wind turbines based on NREL’s FAST[2] is used for aeroelastic modeling. Four annual wind speed distributions are considered; Class I to Class IV defined in IEC61400-2 Ed.2. The result of fatigue load showed that SLM is more sensitive to annual average wind speed than the aeroelastic modeling. And the difference indicated that SLM’s assumption cannot be applied for the researched turbine In addition, the idea of representative operational range around Vdesign which is used in SLM is applied for the result of Aeroelastic model. It is revealed that wider range than the assumption of SLM in most cases. Especially in Class IV, the ranges were unrealistic value.

Diurnal characteristics of the wind potential along the North-western coast of Senegal

This paper is aimed at determining the diurnal characteristics of the wind potential inSenegal. The data used in this study are collected from eight sites (Kayar, Potou, Gandon, Sine Moussa Abdou, Botla, Dara Andal, Nguebeul and Sakhor) located alongside the North-western coast of Senegal. These data cover the period of one year for each site. The annual wind speed distribution curves in all sites were obtained by using the Weibull distribution function. The results obtained show that the power density available is greaterduring the dry season than in the rainy season. In all sites, it appeared more important for the day-time than the night-time. For instance, the power density was 145.30 and 58.34 W/m² at the height of 20 m in the day and night, respectively in the site of Potou; it reveals 99.64 and 52.35 W/m² in the site of Kayar and 103.14 and 45.87 W/m² in Gandon during the same period and the same height. The corresponding average wind speeds are of 5.69 and 4.15 m/s in Potou, 5.04 and 3.80 m/s in Kayar and 5.13 and 3.77 m/s in Gandon. Key words: Weibull distribution, wind potential, diurnal characteristics, seasonal characteristics.

Analysis of wind speed distributions: Wind distribution function derived from minimum cross entropy principles as better alternative to Weibull function

In this study, the minimum cross entropy (MinxEnt) principle is applied for the first time to the wind energy field. This principle allows the inclusion of previous information of a wind speed distribution and covers the maximum entropy (MaxEnt) principle, which is also discussed by Li and Li and Ramirez as special cases in their wind power study. The MinxEnt probability density function (pdf) derived from the MinxEnt principle are used to determine the diurnal, monthly, seasonal and annual wind speed distributions. A comparison between MinxEnt pdfs defined on the basis of the MinxEnt principle and the Weibull pdf on wind speed data, which are taken from different sources and measured in various regions, is conducted. The wind power densities of the considered regions obtained from Weibull and MinxEnt pdfs are also compared. The results indicate that the pdfs derived from the MinxEnt principle fit better to a variety of measured wind speed data than the conventionally applied empirical Weibull pdf. Therefore, it is shown that the MinxEnt principle can be used as an alternative method to estimate both wind distribution and wind power accurately.