Modelling golden eagle habitat selection and flight activity in their home ranges for safer wind farm planning

2018 ◽  
Vol 71 ◽  
pp. 120-131 ◽  
Author(s):  
Hannu Tikkanen ◽  
Seppo Rytkönen ◽  
Olli-Pekka Karlin ◽  
Tuomo Ollila ◽  
Veli-Matti Pakanen ◽  
...  
Author(s):  
David Kidner ◽  
Andrew Sparkes ◽  
Mark Dorey
Keyword(s):  

Author(s):  
Souma Chowdhury ◽  
Jie Zhang ◽  
Achille Messac ◽  
Luciano Castillo

In this paper, we develop a flexible design platform to account for the influences of key factors in optimal planning of commercial scale wind farms. The Unrestricted Wind Farm Layout Optimization (UWFLO) methodology, which avoids limiting assumptions regarding the farm layout and the selection of turbines, is used to develop this design platform. This paper presents critical advancements to the UWFLO methodology to allow the synergistic consideration of (i) the farm layout, (ii) the types of commercial turbines to be installed, and (iii) the expected annual distribution of wind conditions at a particular site. We use a recently developed Kernel Density Estimation (KDE) based method to characterize the multivariate distribution of wind speed and wind direction. Optimization is performed using an advanced mixed discrete Particle Swarm Optimization algorithm. We also implement a high fidelity wind farm cost model that is developed using a Radial Basis Function (RBF) based response surface. The new optimal farm planning platform is applied to design a 25-turbine wind farm at a North Dakota site. We found that the optimal layout is significantly sensitive to the annual variation in wind conditions. Allowing the turbine-types to be selected during optimization was observed to improve the annual energy production by 49% compared to layout optimization alone.


2003 ◽  
Vol 37 (2) ◽  
pp. 292-300 ◽  
Author(s):  
James W. Watson ◽  
Kelly R. McAllister ◽  
D. John Pierce

2005 ◽  
Vol 83 (10) ◽  
pp. 1333-1342 ◽  
Author(s):  
Patrick Morin ◽  
Dominique Berteaux ◽  
Ilya Klvana

In habitat-selection studies, a multi-scale approach is considered necessary to ensure that all elements of selection are depicted and that management decisions accurately reflect the needs of the species under study. We examined hierarchy in summer habitat selection in North American porcupines (Erethizon dorsatum (L., 1758)) in Eastern Canada at the scales of landscape, home range, and single tree. We used radiotelemetry to locate and observe animals visually to record their behaviour and exact location in the habitat. Den use in summer was unexpectedly high for some of our animals, which forced us to use a restricted number of locations per individual for comparison among scales. Although porcupines are generalists at the landscape level, selection patterns appear at the home-range and tree levels. Human-used land and conifer forests were least selected features of home ranges, while deciduous forests dominated by trembling aspen (Populus tremuloides Michx.) and mixed forests were most selected. At the tree scale, trembling aspen was found to be selected over other deciduous trees. However, fruit-producing trees were even more selected. This study shows the importance of a multi-scale approach that includes fine-scale selection.


Refocus ◽  
2002 ◽  
Vol 3 (2) ◽  
pp. 50-51
Author(s):  
Peter Cassidy
Keyword(s):  

Author(s):  
Zhao Hongbo ◽  
Chi Yongning ◽  
Shi Wenhui ◽  
Chen Ziyu ◽  
Wang Yuefeng

2010 ◽  
Vol 143 (8) ◽  
pp. 1827-1828 ◽  
Author(s):  
Guyonne F.E. Janss ◽  
Manuela de Lucas ◽  
D. Philip Whitfield ◽  
Alfonso Lazo ◽  
Miguel Ferrer

2014 ◽  
Vol 705 ◽  
pp. 278-283
Author(s):  
Cong Ying Han ◽  
Rui Yuan Kong ◽  
Tian De Guo ◽  
Wei Pei

Wind energy is now widely used in many countries as a clean energy. In order to make better use of wind energy, we need to study various factors affecting the utilization of wind energy. If we can better predict the wind, we can make full use of wind energy. Where, combing an energy storage system with a wind farm is an effective way to mitigate fluctuations and improve the predictability of wind power. Energy storage sizing has been an important part in wind farm planning. This paper presents an optimization model for determining the capacity of a lead-acid battery integrated with a wind farm. The energy storage capacity calculated in the model gives the lowest cost and has a significant impact on remedying the prediction error. Besides, the charge and discharge operation can also be displayed in our model.


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