Improving an Accuracy of ANN-Based Mesoscale-Microscale Coupling Model by Data Categorization: With Application to Wind Forecast for Offshore and Complex Terrain Onshore Wind Farms

2014 ◽  
pp. 61-66 ◽  
Author(s):  
Alla Sapronova ◽  
Catherine Meissner ◽  
Matteo Mana
Keyword(s):  
Complex Terrain ◽  
Wind Farms ◽  
Coupling Model ◽  
Onshore Wind

scholarly journals Identification and selection of potential sites for onshore wind farms development in Region of Murcia, Spain

Energy ◽  
2014 ◽  
Vol 73 ◽  
pp. 311-324 ◽  
Author(s):  
J.M. Sánchez-Lozano ◽  
M.S. García-Cascales ◽  
M.T. Lamata
Keyword(s):  
Wind Farms ◽  
Onshore Wind ◽  
Selection Of

Community Benefits and UK Offshore Wind Farms: Evolving Convergence in a Divergent Practice

2021 ◽  
pp. 2150001
Author(s):  
John Glasson
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Host Community ◽  
Offshore Wind ◽  
Energy Industry ◽  
Offshore Wind Farms ◽  
Onshore Wind ◽  
Community Benefits ◽  
The Uk ◽  
Renewable Energy Industry

The Offshore Wind sector is a major, dynamic, and rapidly evolving renewable energy industry. This is particularly so in Europe, and especially in the UK. Associated with the growth of the industry has been a growth of interest in community benefits as voluntary measures provided by a developer to the host community. However, in many cases, and for some of the large North Sea distant offshore wind farms, the benefits packages have been disparate and pro rata much smaller than for the well-established onshore wind farm industry. However, there are signs of change. This paper explores the issues of community benefits for the UK offshore sector and evolving practice, as reflected in a macro study of the adoption of community benefits approaches across the industry. This is followed by a more in-depth micro- approach, which explores approaches that have been adopted in three case studies of recent OWF projects — Aberdeen, Beatrice and the Hornsea Array. Whilst there is still much divergence in practice, there are also examples of some convergence, and the development of a more replicable practice. Particularly notable is the adoption of annual community benefits funds, as the key element of community benefits schemes/agreements between developers, local authorities and local communities.

Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations

2019 ◽  
Vol 135 ◽  
pp. 674-686 ◽  
Author(s):  
Miguel A. Prósper ◽  
Carlos Otero-Casal ◽  
Felipe Canoura Fernández ◽  
Gonzalo Miguez-Macho
Keyword(s):  
High Resolution ◽  
Wind Power ◽  
Complex Terrain ◽  
Wind Farm ◽  
Onshore Wind ◽  
Wind Power Forecasting ◽  
Power Forecasting

scholarly journals Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy

2020 ◽  
Author(s):  
Bart M. Doekemeijer ◽  
Stefan Kern ◽  
Sivateja Maturu ◽  
Stoyan Kanev ◽  
Bastian Salbert ◽  
...  
Keyword(s):  
Field Experiment ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Production ◽  
Field Trials ◽  
Open Loop ◽  
Onshore Wind ◽  
Power Maximization ◽  
Energy Gains ◽  
Turbine Arrays

Abstract. The concept of wake steering in wind farms for power maximization has gained significant popularity over the last decade. Recent field trials described in the literature demonstrate the real potential of wake steering on commercial wind farms, but also show that wake steering does not yet consistently lead to an increase in energy production for all inflow conditions. Moreover, a recent survey among experts shows that validation of the concept remains the largest barrier for adoption currently. In response, this article presents the results of a field experiment investigating wake steering in three-turbine arrays at an onshore wind farm in Italy. This experiment was performed as part of the European CL-Windcon project. The measurements show increases in power production of up to 35 % for two-turbine interactions and up to 16 % for three-turbine interactions. However, losses in power production are seen for various regions of wind directions too. In addition to the gains achieved through wake steering at downstream turbines, more interesting to note is that a significant share in gains are from the upstream turbines, showing an increased power production of the yawed turbine itself compared to baseline operation for some wind directions. Furthermore, the surrogate model, while capturing the general trends of wake interaction, lacks the details necessary to accurately represent the measurements. This article supports the notion that further research is necessary, notably on the topics of wind farm modeling and experiment design, before wake steering will lead to consistent energy gains in commercial wind farms.

The analysis of turbulence intensity based on wind speed data in onshore wind farms

2018 ◽  
Vol 123 ◽  
pp. 756-766 ◽  
Author(s):  
Guorui Ren ◽  
Jinfu Liu ◽  
Jie Wan ◽  
Fei Li ◽  
Yufeng Guo ◽  
...  
Keyword(s):  
Wind Speed ◽  
Turbulence Intensity ◽  
Wind Farms ◽  
Onshore Wind ◽  
Wind Speed Data

Overview of Wind Turbine Field Failure Databases: A Discussion of the Requirements for an Analysis

2018 ◽  
Author(s):  
Roozbeh Bakhshi ◽  
Peter Sandborn
Keyword(s):  
Statistical Analysis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farms ◽  
Onshore Wind ◽  
Failure Times ◽  
Reliability Parameters ◽  
Failure Data ◽  
Offshore Installations ◽  
Field Failure

With renewable energy and wind energy in particular becoming mainstream means of energy production, the reliability aspect of wind turbines and their sub-assemblies has become a topic of interest for owners and manufacturers of wind turbines. Operation and Maintenance (O&M) costs account for more than 25% of total costs of onshore wind projects and these costs are even higher for offshore installations. Effective management of O&M costs depends on accurate failure prediction for turbine sub-assemblies. There are numerous models that predict failure times and O&M costs of wind farms. All these models have inputs in the form of reliability parameters. These parameters are usually generated by researchers using field failure data. There are several databases that report the failure data of operating wind turbines and researches use these failure data to generate the reliability parameters through various methods of statistical analysis. However, in order to perform the statistical analysis or use the results of the analysis, one must understand the underlying assumptions of the database along with information about the wind turbine population in the database such as their power rating, age, etc. In this work, we analyze the relevant assumptions and discuss what information is required from a database in order to improve the statistical analysis on wind turbines’ failure data.

scholarly journals An Optimization Framework for Wind Farm Design in Complex Terrain

2018 ◽  
Vol 8 (11) ◽  
pp. 2053 ◽  
Author(s):  
Ju Feng ◽  
Wen Shen ◽  
Ye Li
Keyword(s):  
Complex Terrain ◽  
Wind Farm ◽  
Search Algorithm ◽  
Random Search ◽  
Wind Farms ◽  
Minimal Distance ◽  
Original Design ◽  
Optimization Framework ◽  
Random Search Algorithm ◽  
Wind Resource

Designing wind farms in complex terrain is an important task, especially for countries with a large portion of complex terrain territory. To tackle this task, an optimization framework is developed in this study, which combines the solution from a wind resource assessment tool, an engineering wake model adapted for complex terrain, and an advanced wind farm layout optimization algorithm. Various realistic constraints are modelled and considered, such as the inclusive and exclusive boundaries, minimal distances between turbines, and specific requirements on wind resource and terrain conditions. The default objective function in this framework is the total net annual energy production (AEP) of the wind farm, and the Random Search algorithm is employed to solve the optimization problem. A new algorithm called Heuristic Fill is also developed in this study to find good initial layouts for optimizing wind farms in complex terrain. The ability of the framework is demonstrated in a case study based on a real wind farm with 25 turbines in complex terrain. Results show that the framework can find a better design, with 2.70% higher net AEP than the original design, while keeping the occupied area and minimal distance between turbines at the same level. Comparison with two popular algorithms (Particle Swarm Optimization and Genetic Algorithm) also shows the superiority of the Random Search algorithm.

Sign in / Sign up

Export Citation Format

Share Document