Wind energy calculation methods pdf

Yield losses : For each component fill the appropriate ratio according to your system :. Mechanical losses : Blades : between 0,2 and 0,85 Gear box : between 0,7 and 0, Electrical losses : Generator : between 0,85 and 0,98 Transformer : between 0,92 and 0,98 Rectifier : between 0,9 and 1 1 if no batteries Batteries : between 0,7 and 1 1 if no batteries Wire losses : between 0,9 and 0,99 Total losses ratio : Real power in output of windturbine : kVA losses included.

This excel file will help you to calculate power and electricity production of wind turbines. Download Excel calculator for wind turbine power and energy. Calculation of Wind power and energy Principle A windturbine is composed of rotor often fitted with several blades. Calculator Enter your own values in the white boxes, results are displayed in the green boxes. Excel file to calculate wind turbine power and energy This excel file will help you to calculate power and electricity production of wind turbines.

More articles Primarily, potential current deformations caused by the land topography shall be calculated. The speed perturbation is where is the potential and is the three-dimensional speed deformation vector. For a given radius , for the potential flow at polar coordinates, the following equation shall be written: where is the random coefficient, , is the level Bessel function, is the radius, is the azimuth, is the height, and s , which is zero.

For the specific problems, coefficients shall be calculated from surface kinematical limit conditions: where , is the level Bessel function; s i , which is zero; and is the radius. Functions are as Fourier—Bessel series. The model forms the gray data on the contour lines on the topographical map. Sensitivity of the model depends on the density of contours. In order to make the wind power more competitive against other power generation methods, bigger wind turbines are being designed and established on the clusters especially on the overseas regions called as the wind plant [ 9 ].

To be able to establish a wind power plant, both meteorological and financial parameters are needed. This also shows that one wind plant establishment actually requires to think and use more than one discipline at the same time. This study is aimed at finding out the average wind speed, average power density, energy yield potential obtained as a result of micrositing, capacity factor, amortization period, and unit cost price required for the establishment of the wind power plant as a result of the performed analysis.

These obtained values are being planned to be used to generate a compliance factor for the establishment of the wind power plant by scaling in the rule basis prepared in the fuzzy logic method. We can examine the province in three parts in terms of surface features: i Mountainous areas in the western and northern parts of the province ii The section east of the district center iii Medium-height and slightly rugged Arapgir rubble forming the southern part [ 10 , 11 ].

As a result of continental climate, most part of the areas of the province are coated with steppe. This means that forest land is very small. Existing forest lands are not in good quality.

These are mostly very small oak forests. In this study, topographical obstacles have been taken into consideration [ 10 , 11 ]. When the table is examined, the highest average wind speed is obtained as 4. In Table 2 , average wind severity and average power density values belonging to the region obtained as a result of the analysis made in WAsP are being shown. Most frequently, the distribution used for the calculation of wind power potential is the Weibull distribution.

This distribution has been found by the Swedish physicist Waloddi Weibull. This distribution is considerably flexible and simple and also complies with the real data. In other words, since the Weibull distribution is in compliance with wind speed data, it is generally accepted in wind power analysis [ 12 ].

The Weibull distribution function is as follows [ 13 ]: where is the figure parameter parameter showing the wind speed distribution form , is the scale parameter relative cumulative frequency for wind speed , and is the possibility density function of wind speed. In order to find Weibull parameters and , wind data of the land are required.

Analytical and experimental equations used to find Weibull parameters are written as follows [ 14 ]: where is the standard deviation and is the average speed. After examining the wind data in detail and performing the above-given calculations, according to the Weibull distribution in Figure 4 , the average wind speed is found as 2.

Wind speed measurements are generally being made at different heights than at the tower heights of wind turbines. As known, by using the Hellmann coefficient, estimated wind speed values at a requested height may be calculated from the wind speed values measured at a specific height. Wind speed data measured at a specific height may be transferred to other heights by using the following equation: where is the wind speed at the height to be calculated, is the wind speed at the height where the measurement results are known, is the height of the point to be calculated from the surface, is the height of the point where the measurement results are known from the surface, and is the Hellmann coefficient.

However, the other ways can be used to determine it more accurately [ 15 ]. Calculation in terms of roughness length:.

Calculation in terms of speed and height:. Calculation of roughness length and speed:. By using the existing data, the change of the Hellmann coefficient as per the direction has been examined in addition to the analysis made according to the wind flow direction.

The average of the Hellmann coefficient has been calculated as 0. As shown in Figure 5 , the highest Hellmann coefficient has been found in SE which is the dominant wind direction. Data imported into WAsP are being analyzed, and the average wind severity map of the region in Figure 6 and power density map of the area in Figure 7 have been visually obtained.

The wind turbine-locating process shall be carried out on the points where the wind severity is high by using the average wind speed calculated in the region. While planning the installation of wind turbines in the site, wind atlas is being used. By using WAsP and wind atlas statistics, locations where the power generation amount will be high on the digital map may be defined from colour distribution. In order to minimize the interference of turbines with each other, the micrositing study has been performed.

During this micrositing study, design has been made so as to keep the track area losses of the turbines on each other in the minimum level. Furthermore, while designing the optimum turbine placement so as to obtain a maximum yield, wind turbines have been placed on the dominant wind direction not only taking into consideration the generation amount but also the operability limits of the turbines in technical terms.

As a result of the interference of turbines with each other, it has been understood that there will be a 7. In Table 4 , generation and loss values for each turbine are given for the micrositing study where 6 V80 wind turbines have been used.

As can be shown in Table 4 , when annual net power generation values are being considered, the maximum generation amount 7. During the micrositing study, it has been calculated that the maximum wake loss 9. The capacity factor, which is an important parameter and has to be known both by the generators and consumers, is the division of energy generated in a specific time frame to the maximum energy that can be generated at that specific time frame [ 16 , 17 ].

In this study, the capacity factor has been calculated for the turbine used during micrositing and turbine selection studies and generated micrositings: where is the capacity factor, is the generated total power, is the nominal power value, and is the time.

One of the most important studies that have to be carried out while establishing a wind turbine to a region is the calculation of kWh power cost. Generally, the cost of one wind power project per kWh is found by proportioning the annual total cost to the annual power generation amount. The annual power generation amount changes depending on the parameters such as the hub height of turbine, rotor diameter, average wind severity of the area, and annual cost may be correlated with turbine price, turbine foundations, inner site road construction costs, investment costs, and project lifetime.

In Figure 9 , the cost structure is shown per kWh for wind power projects [ 19 ]. In this study, within the first establishment, turbine cost, network connection, foundation and establishment costs, electricity installation, and road construction and control systems have been considered as mentioned in the report of the European Wind Power Union, and land rental, consultancy, maintenance and repair, and yearly licence costs have been included into the analysis as permanently paid costs within years Table 6.

Annual In this case, annual income has been calculated as 2. Emission sales income for the As a result of the calculations made, annual income and expenses as well as the annual margin are shown in Table 7. In this study, operational expenses, maintenance, repair, insurance, rental, and licence costs, and some values changing as per year such as interest have been reflected as average values in the income-expense table by taking into consideration the project lifetime.

In case of selling the electricity at 5. In this study, cost per kWh for the capacity factor obtained from selected turbines has been calculated. By using the capacity factor as In order to calculate the unit cost of electricity power obtained from wind turbines, it is necessary to know the regaining factor of investment.

In the calculations made, unit cost values to be used as the input unit in the fuzzy logic system are found as 4. In the analysis made, the average wind speed is found as 2. The change of the Hellmann coefficient as per the direction by using existing data in addition to the analysis made as per the wind blowing has been examined, and the average Hellmann coefficient has been calculated as 0. It has been understood that 7.

Annual generation values of the plant have been found as 4. The amortization period is calculated as 7 years. The data used to support the findings of this study are available from the corresponding author upon request.

The authors certify that there are no actual or potential conflicts of interest in relation to this article. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Article of the Year Award: Outstanding research contributions of , as selected by our Chief Editors.

Read the winning articles. Journal overview. Special Issues. Academic Editor: Ismail Gultepe. Received 07 Feb Revised 05 Apr Accepted 24 Apr Published 30 May Abstract 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.

Introduction Various methods are being used for the determination of wind power potentials. General Purposes of WAsP It is possible to group them under four main titles of general purpose of WAsP so as to analyze the raw data, generate the wind atlas, and assess the wind climate and wind energy potential. Roughness Exchange Model The logarithmic wind profile is only applicable where the surface is homogeneous.

Shelter Model The wind profile is deformed at close distances and at lower parts of the flow of wind coming from the turbine. Orographic Model In this model, on the measured wind, data faults of local impacts caused by the topographical structure are being corrected [ 8 ].