Analysis of long distance wakes behind a row of turbines
For this research study, long term wind data from a wind farm will be collected from the measurements available at 3 km and 6 km east of the wind farm for a row of 10 turbines. These data sets are presented in UPWIND report. All these data sets have 10 minutes mean values. For analyzing this data, analytical analysis will be used by using the SPSS technique that will help to provide the descriptive statistics and regression.
The motivation behind conducting this research was that to develop strategies that will help to analyze the long distance wakes behind a row of turbines. Another motivation is that the researcher is having due consideration regarding the environmental concerns and want to make career in the field of energy. This is the reason why social and environmental aspects motivated the researcher to conduct the research on this topic.
This research is significant for analyzing the long distance wakes behind a row of turbines. It also helps to analyse the impact of using the various internal turbine distances. It helped to get an outlook of the power production and also the velocity deficit in the farm wake. At the same time, it helped in determining sensitivity to different parameters such as, grid resolution and characteristics of turbulence.
The below research question will be answered:
- What is the impact of long distance wakes behind the row of turbines?
The major aim of this research is to analyze the long distance wake behind the row of 10 turbines in order to forecast wake recovery. For this, the following objectives will be achieved:
- To determine sensitivity to different parameters including Reynolds number, grid resolution and turbulence characteristics
- To determine the impact of using different internal turbine distances
- To study the power production and the velocity deficit in the farm wake
For analysing this data, analytical analysis will be used by using the SPSS technique that will help to provide the descriptive statistics and regression. Apart from this, simulation method will also be preferred to estimate the production in the farm and velocity deficit at different distances of wakes. The simulations will be conducted according to the turbulence and wind shear conditions of the site. There will be a comparison between the measured production data and wind data in the wake.
Troldborg, et al. (2014) defined that large offshore wind farms support to produce long distance wakes. In like manner, increase in number of offshore wind farms, increases the chances of interaction with one wind farms to other neighbouring wind farms. It enables to wake from one wind farm to another. According to Almazyad, et al. (2014), majorly larger wind farms are planned at the offshore location as it remains the most suitable sites to build the wind farms. Due to this reason, on these locations, the clusters of wind farm can be seen as these wind farms are constructed near to each other.
In this, different parameters are studied by different researcher in order to understand the importance and characteristics of the turbulence, Reynolds number and grid resolution for measuring the long distance wakes. Nygaard (2014) analysed that behind these wind turbines in the wind farms, there is a disrupted flow of air which is known as wake which can be characterized as the reduced speed of wind while increasing the turbulence. The individual turbine wakes are combined with each other for the purpose of forming a farm wake which remains supportive to travel at a longer distance. Moreover, in wind farm, clusters farm to farm interaction also takes place.
Due to which, the long distance wake of a wind farm also impacts the wind conditions of other wind farms too which are built in the surrounding area (Witha, et al., 2014).
The wind turbulence works on the Mann model and according to which fluctuating body forces the wind farm upstream. Moreover, under this model, neutral atmosphere is assumed but it is identified that this model remains assistive to study wake effects inside the farms but not support to evaluate longer distances which are needed in the context of farm to farm interaction. To evaluate the long distance wakes, various numerical studies have taken place.
On the other hand, Nygaard (2014) determined that to get the better understanding regarding long distance wakes and a row of turbines, there is a need of predicting the wake recovery in an accurate manner.
In this context, numerical simulations as well as measurements in the wake remain assistive. In the perspective of earlier studies, it is identified that the wake can be done by utilizing simplified wake models. It includes the models which are applied for the purpose of momentum equation, roughness elements while representing the turbines. According to the views of Almazyad, et al. (2014), there is a quantifiable relationship between wind farm efficiency and wind speed. In like manner, the direction of the turbines, turbulence and atmospheric stability also play vital role in the generation of power output.
Moreover, Witha, et al. (2014) identified that wake losses are majorly take place due to strong wind speed variations in the context of turbine thrust coefficient; direction, atmospheric stability and turbulence plays vital role. In like manner, the efficiency of wind farm remains highly dependent on the distribution of wind speeds as well as wind direction.
According to Barthelmie & Jensen (2010), grid resolution is defined as a tool which helps in measuring the minimum distance between the two different objects. In simple words, resolution is stated as pixel whereas grid is stated as a cell size and these both helps in determining the impact of one object over the other. On the other hand, Breton, et al., (2012) also that Reynolds number is third parameters which help in measuring the power coefficient by determining the turbines value. While studying, it is also determined that decrease in turbine value leads to the increase in the Reynolds number from 1000 to 10000.
For analyzing the long distance wakes, different approach is used by the researcher i.e., actuator disc approach which also helps in measuring and determining the impact of the row turbine due to long distance wakes. Using the actuator disc approach, helped in researcher in studying the ability of simulate model for wake recovery in concern to long distance wake behind a row turbines. Wu & Porté-Agel (2012) defined actuator disc approach as an approach which is used by different research for predicting the wakes tide and performance of the wind turbine in the turbulence area. This approach is also known as RANS approach which provides accurate result by using the small scale model of wind turbine in order to measure the performance of wakes in the farm.
The impact of long distances wakes on the internal turbine distance is clear as the wind shear of turbine creates intensity towards the domain needs of the incoming flow. In the research study of Troldborg, et al., (2011), it has been clearly stated that lower turbulence creates low recovery as expected which means that the impact of internal turbine is minor on the farm wake recovery. The current study investigates different parameters such as grid resolution which creates difference between the characteristics turbulence and internal turbine spacing. The grids resolution helps in studying the impact of the internal turbine over the turbulence intensity.
While studying, it is also observed that the impact of resolution is high on the internal turbine spacing and this also creates impact on the long distance wakes which are behind a row turbine. In this parameter study, the turbulence intensity is also measured in order to determine the impact on the breakdown of wake and mixing of flow. At the same time, Nilsson, et al., (2015) also stated that while determining the impact of the internal turbine spacing, the impacts are measured on the basis of rate of recovery and power deficit. In this study, different parameters helped in determining the impact of wake on the internal turbine spacing, turbulence intensity and relative production.
Troldborg, et al. (2014) depicted that every large offshore wind farm has a long distance wake as increased number of offshore wind farms supports to increase more occasions regarding interaction of wake from one wind farm to another. So, it can be stated that long distance wakes directly impact the wind conditions at the neighbouring sites. In addition to this, Gaumond, et al., (2014) also stated that there is high impact of turbulence over the wake recovery and relative production. This impact arises because of turbulence levels which creating impact on the background levels and on the farm wake.
Eriksson, et al. (2014) illustrated that the impact of turbine spacing towards wake losses is highly uncertain in nature and in this perspective, it is identified that there is an evidence of deep array effect which reflects that wake losses in the centre of the wind farm are generally remains under-estimated while focusing towards wind farm model. However, it is identified that the overall efficiency of the wind farm can be evaluated on the basis of prediction as it compensates the edge effects.
Moreover, Lu & Porté-Agel (2011) also stated that behind a wind turbine there is a wake which creates impact on the productivity as well as reduce the wind speed if wake are near to each other. The most critical impact is the distance between the tower of turbine which creates simulation and that originates from the turbine which are need to be coordinated with each other. While analyzing the long distances of turbines, it is found that there is need to understand the wake effect inside the farm in order to determine the impact on the internal turbine distances.
The internal turbine distances in the farm are required to be placed at some distances in order to increase the wind speed and extract more energy.
For studying the power production and velocity deficit in the farm wake, simulation is carried out by the researcher. Lubitz (2014) explained that simulated power production will helps in creating a good correlation with the real production inside the farm in order to measure the downstream rows of turbine. This use of simulation helps in over predicting the wake recovery, wind velocity and long distance behind the row of turbine in the farm. In the research study, Porté-Agel, et al., (2011) clearly stated that using the simulation in the measuring and determining the long distances wake in the wind turbine. This is a presented method which can be applied for simulation for long distance wakes.
The effect of power production in the farm wake is predicted on the basis of velocity deficit which also helps in determining the neighbourhood effects in measuring the wind farm performance. According to Lydia, et al., (2014), the effect of wind turbine develops an interaction on the power production of wind turbulence and its performance due to offshore wind farms. In context to this, Hirth & Müller (2016) explained that in offshore wind farms, power production is deteriorated due to the mutual influence of wind flow around the wind turbines is resulted into a serious issue.
The major objective of this research is to analyze of long distance wakes behind a row of turbines. At the same time, the significance of this chapter is to analyze the various approaches and methods which are been used for the goal of the research study along with full justification on the topic of the research (Mackey & Gass, 2015). The research opinion framework is used for choosing the appropriate research methodology that is vital for the researcher in selecting most prominent elements of this study for accomplishment of the research.
This part is based on the views of the researcher on the research issues. It provides help to the researcher by building the skills, abilities and knowledge regarding the issues of the research. At the same time, there are three kinds of research philosophies. The three research philosophies are realism, positivism, and interpretivism research philosophy. In like manner, realism research philosophy is one which is based on the reality of the existing phenomena. On the other hand, positivism research philosophy is one which is been used by the research to gain an in-depth understanding in regard of the issues of research (Mkansi, M., & Acheampong, 2012). The positivism philosophy is subjective in nature and it is analyzed on facts and figure scientifically. However, interpretivism research philosophy is one which is not subjective in nature. It helps the researcher in developing an understanding in the context of research issues. For the purpose of this research study, the interpretivism research philosophy is used to interpret the different viewpoints and opinions of the participants in the research (Chen, et al., 2011).
The research process is customized by using different choices like, quantitative method, qualitative method and the mixed method which combines the two other methods. The quantitative method is based on the numerical values. On the other hand, the qualitative method uses the ideas, opinions, thoughts, etc in the research. Mixed method is the combination of the quantitative and the qualitative method. In regard of this research study, the researcher has used the quantitative method which has helped the researcher to generate the valid outcomes of the research as well as accomplish the research objectives on time (Lewis, 2015).
The data collection method is a process which helps the researcher to collect and gather the information and data from the various types of sources. There are two different kinds of data collection methods or sources which help to gather data and information. These data collection methods also help the researcher to find out best possible solutions in the context of the research problems or the issues of the research to evaluate the outcomes of the research and to measure the hypothesis. The two sources used for collecting data are primary data collection method and secondary data collection method (Aitken, et al., 2011).
Primary data collection method: Primary data collection method is also referred to as the first hand data. Primary data is the data which is collected by the researcher for the first time during his research study. It is the fresh data which is not been used yet and is gathered for the first time. The different ways for gathering the primary data are questionnaire, survey, case study, interviews, etc.
Secondary data collection method: Secondary data collection method is also called as the second hand data. Secondary data is the data which is already available and is used by the researcher. The methods which are used by the researcher to gather the data includes, books, magazines, company websites, journals, articles, etc. These methods help the researcher to gather the data and information. In the context of this research study, the researcher has collected the data with the help of the secondary data collection method i.e. by books, articles, journals, etc (Palinkas, et al., 2015).
For obtaining the valid and accurate research outcomes this method is selected by the researcher to analyze the data which is collected as well as the information used. There are different kinds of tools which are adopted by the researcher to analyze the data collected. The different types of tools which are adopted by the researcher are statistical analysis, cluster analysis, and the factor analysis. The researcher has used the graphs and tables to represent the collected data which was not possible by adopting the other methods. Additionally, the researcher was benefited by using this as it helped to accomplish the objectives of the research and also to gather the outcomes of the research on time and successfully (Robinson, et al., 2010).
At the end it is concluded that, for accomplishing the research on time and successfully there are different types or kinds of aspects which are been adopted by the researcher for successful accomplishment of the research. The aspect includes interpretivism research philosophy, secondary data collection method, and quantitative research design (Smith, 2015).
Aitken, L. M., Marshall, A., Elliott, R., & McKinley, S. (2011). Comparison of ‘think aloud’and observation as data collection methods in the study of decision making regarding sedation in intensive care patients. International Journal of Nursing Studies, 48(3), 318-325.
Almazyad, A. S., Seddiq, Y. M., Alotaibi, A. M., Al-Nasheri, A. Y., BenSaleh, M. S., Obeid, A. M., & Qasim, S. M. (2014). A proposed scalable design and simulation of wireless sensor network-based long-distance water pipeline leakage monitoring system. Sensors, 14(2), 3557-3577.
Barthelmie, R. J., & Jensen, L. E. (2010). Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm. Wind Energy, 13(6), 573-586.
Breton, S. P., Nilsson, K., Ivanell, S., Olivares-Espinosa, H., Masson, C., & Dufresne, L. (2012). Study of the effect of the presence of downstream turbines on upstream ones and use of a controller in CFD wind turbine simulation models. In The Science of Making Torque from Wind.
Chen, Y. Y., Shek, D. T., & Bu, F. F. (2011). Applications of interpretive and constructionist research methods in adolescent research: philosophy, principles and examples. International journal of adolescent medicine and health, 23(2), 129-139.
Eriksson, O., Nilsson, K., Breton, S. P., & Ivanell, S. (2014). Analysis of long distance wakes behind a row of turbines–a parameter study. In Journal of Physics: Conference Series (Vol. 524, No. 1, p. 012152). IOP Publishing.
Gaumond, M., Réthoré, P. E., Ott, S., Pena, A., Bechmann, A., & Hansen, K. S. (2014). Evaluation of the wind direction uncertainty and its impact on wake modeling at the Horns Rev offshore wind farm. Wind Energy, 17(8), 1169-1178.
Hirth, L., & Müller, S. (2016). System-friendly wind power: How advanced wind turbine design can increase the economic value of electricity generated through wind power. Energy Economics, 56, 51-63.
Lewis, S. (2015). Qualitative inquiry and research design: Choosing among five approaches. Health promotion practice, 16(4), 473-475.
Lu, H., & Porté-Agel, F. (2011). Large-eddy simulation of a very large wind farm in a stable atmospheric boundary layer. Physics of Fluids, 23(6), 065101.
Lubitz, W. D. (2014). Impact of ambient turbulence on performance of a small wind turbine. Renewable Energy, 61, 69-73.
Lydia, M., Kumar, S. S., Selvakumar, A. I., & Kumar, G. E. P. (2014). A comprehensive review on wind turbine power curve modeling techniques. Renewable and Sustainable Energy Reviews, 30, 452-460.
Mackey, A., & Gass, S. M. (2015). Second language research: Methodology and design. Routledge.
Mkansi, M., & Acheampong, E. A. (2012). Research philosophy debates and classifications: students’ dilemma. Electronic Journal of Business Research Methods, 10(2), 132-140.
Nilsson, K., Ivanell, S., Hansen, K. S., Mikkelsen, R., Sørensen, J. N., Breton, S. P., & Henningson, D. (2015). Large‐eddy simulations of the Lillgrund wind farm. Wind Energy, 18(3), 449-467.
Nygaard, N. G. (2014). Wakes in very large wind farms and the effect of neighbouring wind farms. In Journal of Physics: Conference Series (Vol. 524, No. 1, p. 012162). IOP Publishing.
Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533-544.
Porté-Agel, F., Wu, Y. T., Lu, H., & Conzemius, R. J. (2011). Large-eddy simulation of atmospheric boundary layer flow through wind turbines and wind farms. Journal of Wind Engineering and Industrial Aerodynamics, 99(4), 154-168.
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139-140.
Smith, J. A. (Ed.). (2015). Qualitative psychology: A practical guide to research methods. Sage.
Troldborg, N., Larsen, G. C., Madsen, H. A., Hansen, K. S., Sørensen, J. N., & Mikkelsen, R. (2011). Numerical simulations of wake interaction between two wind turbines at various inflow conditions. Wind Energy, 14(7), 859-876.
Troldborg, N., Sørensen, J. N., Mikkelsen, R., & Sørensen, N. N. (2014). A simple atmospheric boundary layer model applied to large eddy simulations of wind turbine wakes. Wind Energy, 17(4), 657-669.
Witha, B., Steinfeld, G., Dörenkämper, M., & Heinemann, D. (2014). Large-eddy simulation of multiple wakes in offshore wind farms. In Journal of Physics: Conference Series (Vol. 555, No. 1, p. 012108). IOP Publishing.
Wu, Y. T., & Porté-Agel, F. (2012). Atmospheric turbulence effects on wind-turbine wakes: An LES study. Energies, 5(12), 5340-5362.