A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm

2021 ◽  
Vol 236 ◽  
pp. 114002
Author(s):  
Mehdi Neshat ◽  
Meysam Majidi Nezhad ◽  
Ehsan Abbasnejad ◽  
Seyedali Mirjalili ◽  
Lina Bertling Tjernberg ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Wind Farm ◽  
Evolutionary Model ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Short Term ◽  
Wind Speed Forecasting

scholarly journals Wind Farm Power Production Assessment: Introduction of a New Actuator Disc Method and Comparison with Existing Models in the Context of a Case Study

2019 ◽  
Vol 9 (3) ◽  
pp. 431 ◽  
Author(s):  
Nikolaos Simisiroglou ◽  
Heracles Polatidis ◽  
Stefan Ivanell
Keyword(s):  
Wind Farm ◽  
Power Production ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Actuator Disc ◽  
Power Deficit ◽  
The Given ◽  
Method Show ◽  
Do So

The aim of the present study is to perform a comparative analysis of two actuator disc methods (ACD) and two analytical wake models for wind farm power production assessment. To do so, wind turbine power production data from the Lillgrund offshore wind farm in Sweden is used. The measured power production for individual wind turbines is compared with results from simulations, done in the WindSim software, using two ACD methods (ACD (2008) and ACD (2016)) and two analytical wake models widely used within the wind industry (Jensen and Larsen wake models). It was found that the ACD (2016) method and the Larsen model outperform the other method and model in most cases. Furthermore, results from the ACD (2016) method show a clear improvement in the estimated power production in comparison to the ACD (2008) method. The Jensen method seems to overestimate the power deficit for all cases. The ACD (2016) method, despite its simplicity, can capture the power production within the given error margin although it tends to underestimate the power deficit.

Towards a Population-based SHM: A Case Study on an Offshore Wind Farm

2015 ◽  
Author(s):  
EVANGELOS PAPATHEOU ◽  
NIKOLAOS DERVILIS ◽  
EOGHAN MAGUIRE ◽  
IFIGENEIA ANTONIADOU ◽  
KEITH WORDEN
Keyword(s):  
Wind Farm ◽  
Population Based ◽  
Offshore Wind ◽  
Offshore Wind Farm

A novel hybrid MCDM approach for offshore wind farm site selection: A case study of Iran

2015 ◽  
Vol 109 ◽  
pp. 17-28 ◽  
Author(s):  
Abdolvahhab Fetanat ◽  
Ehsan Khorasaninejad
Keyword(s):  
Site Selection ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm

scholarly journals Recovery processes in a large offshore wind farm

2021 ◽  
Vol 6 (5) ◽  
pp. 1089-1106
Author(s):  
Tanvi Gupta ◽  
Somnath Baidya Roy
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
High Wind ◽  
Offshore Wind Farm ◽  
High Wind Speed ◽  
Recovery Processes ◽  
Speed Range

Abstract. Wind turbines in a wind farm extract energy from the atmospheric flow and convert it into electricity, resulting in a localized momentum deficit in the wake that reduces energy availability for downwind turbines. Atmospheric momentum convergence from above, below, and the sides into the wakes replenishes the lost momentum, at least partially, so that turbines deep inside a wind farm can continue to function. In this study, we explore recovery processes in a hypothetical offshore wind farm with particular emphasis on comparing the spatial patterns and magnitudes of horizontal- and vertical-recovery processes and understanding the role of mesoscale processes in momentum recovery in wind farms. For this purpose, we use the Weather Research and Forecasting (WRF) model, a state-of-the-art mesoscale model equipped with a wind turbine parameterization, to simulate a hypothetical large offshore wind farm with different wind turbine spacings under realistic initial and boundary conditions. Different inter-turbine spacings range from a densely packed wind farm (case I: low inter-turbine distance of 0.5 km ∼ 5 rotor diameter) to a sparsely packed wind farm (case III: high inter-turbine distance of 2 km ∼ 20 rotor diameter). In this study, apart from the inter-turbine spacings, we also explored the role of different ranges of background wind speeds over which the wind turbines operate, ranging from a low wind speed range of 3–11.75 m s−1 (case A) to a high wind speed range of 11–18 m s−1 (case C). Results show that vertical turbulent transport of momentum from aloft is the main contributor to recovery in wind farms except in cases with high-wind-speed range and sparsely packed wind farms, where horizontal advective momentum transport can also contribute equally. Vertical recovery shows a systematic dependence on wind speed and wind farm density that is quantified using low-order empirical equations. Wind farms significantly alter the mesoscale flow patterns, especially for densely packed wind farms under high-wind-speed conditions. In these cases, the mesoscale circulations created by the wind farms can transport high-momentum air from aloft into the atmospheric boundary layer (ABL) and thus aid in recovery in wind farms. To the best of our knowledge, this is one of the first studies to look at wind farm replenishment processes under realistic meteorological conditions including the role of mesoscale processes. Overall, this study advances our understanding of recovery processes in wind farms and wind farm–ABL interactions.

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