Optimal Power Dispatch of an Offshore Wind Farm under Generator Fault

For offshore wind farms, the power loss caused by the wake effect is large due to the large capacity of the wind turbine. At the same time, the operating environment of the offshore wind farm is very harsh, and the cost of maintenance is higher than that of the onshore wind farm. Therefore, it is worthwhile to study through reasonable control how to reduce the wake loss of the wind farm and minimize the losses caused by the fault. In this paper, the Particle Swarm Optimization (PSO) algorithm is used to optimize the active power dispatch of wind farms under generator cooling system faults. The optimization objectives include avoiding the further deterioration of the generator fault, reducing unnecessary power loss of the faulty wind turbine, tracking the power demand from the Transmission System Operator (TSO), and reducing the power fluctuation caused by the PSO algorithm. The proposed optimal power dispatch strategy was compared with the two generally-used fault-handling methods and the proportional dispatch strategy in simulation. The result shows that the proposed strategy can improve the power generation capacity of the wind farm and achieve an efficient trade-off between power generation and fault protection.

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FLOWSTAR-Energy: a high resolution wind farm wake model

10.5194/wes-2016-34 ◽

2016 ◽

Author(s):

Amy Stidworthy ◽

David Carruthers

Keyword(s):

Boundary Layer ◽

Wind Turbine ◽

Complex Terrain ◽

Wind Farm ◽

Wind Farms ◽

Measured Data ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Wake Model ◽

Turbine Wake

Abstract. A new model, FLOWSTAR-Energy, has been developed for the practical calculation of wind farm energy production. It includes a semi-analytic model for airflow over complex surfaces (FLOWSTAR) and a wind turbine wake model that simulates wake-wake interaction by exploiting some similarities between the decay of a wind turbine wake and the dispersion of plume of passive gas emitted from an elevated source. Additional turbulence due to the wind shear at the wake edge is included and the assumption is made that wind turbines are only affected by wakes from upstream wind turbines. The model takes account of the structure of the atmospheric boundary layer, which means that the effect of atmospheric stability is included. A marine boundary layer scheme is also included to enable offshore as well as onshore sites to be modelled. FLOWSTAR-Energy has been used to model three different wind farms and the predicted energy output compared with measured data. Maps of wind speed and turbulence have also been calculated for two of the wind farms. The Tjaæreborg wind farm is an onshore site consisting of a single 2 MW wind turbine, the NoordZee offshore wind farm consists of 36 V90 VESTAS 3 MW turbines and the Nysted offshore wind farm consists of 72 Bonus 2.3 MW turbines. The NoordZee and Nysted measurement datasets include stability distribution data, which was included in the modelling. Of the two offshore wind farm datasets, the Noordzee dataset focuses on a single 5-degree wind direction sector and therefore only represents a limited number of measurements (1,284); whereas the Nysted dataset captures data for seven 5-degree wind direction sectors and represents a larger number of measurements (84,363). The best agreement between modelled and measured data was obtained with the Nysted dataset, with high correlation (0.98 or above) and low normalised mean square error (0.007 or below) for all three flow cases. The results from Tjæreborg show that the model replicates the Gaussian shape of the wake deficit two turbine diameters downstream of the turbine, but the lack of stability information in this dataset makes it difficult to draw conclusions about model performance. One of the key strengths of FLOWSTAR-Energy is its ability to model the effects of complex terrain on the airflow. However, although the airflow model has been previously compared extensively with flow data, it has so far not been used in detail to predict energy yields from wind farms in complex terrain. This will be the subject of a further validation study for FLOWSTAR-Energy.

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Performance Analysis of Offshore Mono-Pile Wind Turbine Co-Located With Floating Photovoltaic Systems in Shark Bay, Australia

10.1115/omae2021-63852 ◽

2021 ◽

Author(s):

Morteza Bahadori ◽

Hassan Ghassemi

Keyword(s):

Wind Turbine ◽

Hybrid System ◽

Wind Farm ◽

Wind Farms ◽

Power Production ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Offshore Wind Farms ◽

Shark Bay

Abstract In recent years, as more offshore wind farms have been constructed, the possibility of integrating various offshore renewable technologies is increased. Using offshore wind and solar power resources as a hybrid system provides several advantages including optimized marine space utilization, reduced maintenance and operation costs, and relieving wind variability on output power. In this research, both offshore wind and solar resources are analyzed based on accurate data through a case study in Shark Bay (Australia), where bathymetric information confirms using offshore bottom-fixed wind turbine regarding the depth of water. Also, the power production of the hybrid system of co-located bottom-fixed wind turbine and floating photovoltaic are investigated with the technical characteristics of commercial mono-pile wind turbine and photovoltaic panels. Despite the offshore wind, the solar energy output has negligible variation across the case study area, therefore using the solar platform in deep water is not an efficient option. It is demonstrated that the floating solar has a power production rate nearly six times more than a typical offshore wind farm with the same occupied area. Also, output energy and surface power density of the hybrid offshore windsolar system are improved significantly compared to a standalone offshore wind farm. The benefits of offshore wind and solar synergies augment the efficiency of current offshore wind farms throughout the world.

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Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore

Energies ◽

10.3390/en14010146 ◽

2020 ◽

Vol 14 (1) ◽

pp. 146

Author(s):

Joongjin Shin ◽

Seokheum Baek ◽

Youngwoo Rhee

Keyword(s):

Wind Farm ◽

Wind Farms ◽

Optimization Procedure ◽

Pso Algorithm ◽

Layout Optimization ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Offshore Wind Farms ◽

Metropolitan City ◽

Wind Potential

This paper examines the solution to the problem of turbine arrangement in offshore wind farms. The two main objectives of offshore wind farm planning are to minimize wake loss and maximize annual energy production (AEP). There is more wind with less turbulence offshore compared with an onshore case, which drives the development of the offshore wind farm worldwide. South Korea’s offshore wind farms, which are deep in water and cannot be installed far off the coast, are affected by land complex terrain. Thus, domestic offshore wind farms should consider the separation distance from the coastline as a major variable depending on the topography and marine environmental characteristics. As a case study, a 60 MW offshore wind farm was optimized for the coast of the Busan Metropolitan City. For the analysis of wind conditions in the candidate site, wind conditions data from the meteorological tower and Ganjeolgot AWS at Gori offshore were used from 2001 to 2018. The optimization procedure is performed by evolutionary algorithm (EA) and particle swarm optimization (PSO) algorithm with the purpose of maximizing the AEP while minimizing the total wake loss. The optimization procedure can be applied to the optimized placement of WTs within a wind farm and can be extended for a variety of wind conditions and wind farm capacity. The results of the optimization were predicted to be 172,437 MWh/year under the Gori offshore wind potential, turbine layout optimization, and an annual utilization rate of 26.5%. This could convert 4.6% of electricity consumption in the Busan Metropolitan City region in 2019 in offshore wind farms.

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Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm

10.5194/wes-2018-62 ◽

2018 ◽

Author(s):

Thomas Duc ◽

Olivier Coupiac ◽

Nicolas Girard ◽

Gregor Giebel ◽

Tuhfe Göçmen

Keyword(s):

Wind Turbine ◽

Wind Farm ◽

Decay Constant ◽

Wind Farms ◽

Power Production ◽

Original Model ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Onshore Wind ◽

Wake Model

Abstract. In this paper, a new calculation procedure to improve the accuracy of the Jensen wake model for operating wind farms is proposed. In this procedure the wake decay constant is updated locally at each wind turbine based on the turbulence intensity measurement provided by the nacelle anemometer. This procedure was tested against experimental data at onshore wind farm La Sole du Moulin Vieux (SMV) in France and the offshore wind farm Horns Rev-I in Denmark. Results indicate that the wake deficit at each wind turbine is described more accurately than when using the original model, reducing the error from 15–20 % to approximately 5 %. Furthermore, this new model properly calibrated for the SMV wind farm is then used for coordinated control purposes. Assuming an axial induction control strategy, and following a model predictive approach, new power settings leading to an increased overall power production of the farm are derived. Power gains found are in the order of 2.5 % for a two wind turbine case with close spacing and 1 to 1.5 % for a row of five wind turbines with a larger spacing. Finally, the uncertainty of the updated Jensen model is quantified considering the model inputs. When checked against the predicted power gain, the uncertainty of the model estimations is seen to be excessive, reaching approximately 4 %, which indicates the difficulty of field observations for such a gain. Nevertheless, the optimized settings are to be implemented during a field test campaign at SMV wind farm in scope of the national project SMARTEOLE.

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Prediction of Power Generation by Offshore Wind Farms Using Multiple Data Sources

Energies ◽

10.3390/en12040700 ◽

2019 ◽

Vol 12 (4) ◽

pp. 700 ◽

Cited By ~ 5

Author(s):

Cheng-Dar Yue ◽

Che-Chih Liu ◽

Chien-Cheng Tu ◽

Ta-Hui Lin

Keyword(s):

Power Generation ◽

Wind Farm ◽

Wind Farms ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Offshore Wind Farms ◽

Offshore Area ◽

Multiple Data ◽

Central Weather Bureau ◽

Wind Resources

In this study we evaluated the wind resources of wind farms in the Changhua offshore area of Taiwan. The offshore wind farm in Zone of Potential (ZoP) 26 was optimized through an economic evaluation. The annual energy production (AEP) of the offshore wind farm in ZoP 26 was predicted for 10 and 25 years with probabilities of 50%, 75%, and 90% by using measured mast data, measure-correlate-predict (MCP) data derived from Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Central Weather Bureau (CWB) data. When the distance between the turbines in a wind farm was decreased from 12D to 6D, the turbine number increased from 53 to 132, while the capacity factor decreased slightly from 48.6% to 47.6%. MCP data derived from the inland CWB station with similar levels of wind resources can be used to accurately predict the power generation of the target offshore wind farm. The use of MCP with mast data as target data, together with CWB and MERRA data as reference data, proved to be a feasible method for predicting offshore wind power generation in places where a mast is available in a neighboring area.

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The Effect of Wind and Wave Misalignment on the Response of a Wind Turbine at Bockstigen

Volume 1: Offshore Technology; Offshore Wind Energy; Ocean Research Technology; LNG Specialty Symposium ◽

10.1115/omae2006-92075 ◽

2006 ◽

Cited By ~ 2

Author(s):

Jenny M. V. Trumars ◽

Johan O. Jonsson ◽

Lars Bergdahl

Keyword(s):

Wind Energy ◽

Wind Turbine ◽

Wind Farm ◽

Wind Wave ◽

Wind Farms ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Wave Load ◽

Design Requirements ◽

The Waves

The aim of this work is to evaluate data from the offshore wind farm Bockstigen in order to study the effect of directional spreading of waves and wind wave misalignment on the response of the structure. The development of offshore wind energy has led to wind farms at sites with water depths ranging from approximately 6 to 30 m. The change of location from land to sea changes the design requirements of wind energy converters. In addition to wind loads, the wave load on the structure has to be taken into account. Since a wind turbine is highly damped in the inline direction as compared to the crosswise direction, the effect of directional spreading of waves on the response is studied. Depending on the dynamics of the structure the crosswise force could give a larger response than the corresponding inline force. In this study the influence of the directional spreading of the waves on the response is not clear, however the effect of wind and wave misalignment is clear.

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Recovery Processes in a Large Offshore Wind Farm

10.5194/wes-2021-7 ◽

2021 ◽

Author(s):

Tanvi Gupta ◽

Somnath Baidya Roy

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Wind Farm ◽

Mesoscale Model ◽

Wind Farms ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Recovery Processes ◽

Mesoscale Processes

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 sides into the wakes replenish 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. Results show that vertical turbulent transport of momentum from aloft is the main contributor to recovery in wind farms except in cases with strong background winds and high inter-turbine spacing where horizontal advective momentum transport can also contribute equally. Vertical recovery shows a systematic dependence on wind speed and wind farm density that can be 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. This is a novel study that is one of the first to look at wind farm replenishment processes under realistic meteorological conditions including the role of mesoscale processes. Overall, this study significantly advances our understanding of recovery processes in wind farms and wind farm-ABL interactions.

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Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm

Wind Energy Science ◽

10.5194/wes-4-287-2019 ◽

2019 ◽

Vol 4 (2) ◽

pp. 287-302 ◽

Cited By ~ 6

Author(s):

Thomas Duc ◽

Olivier Coupiac ◽

Nicolas Girard ◽

Gregor Giebel ◽

Tuhfe Göçmen

Keyword(s):

Wind Turbine ◽

Wind Farm ◽

Decay Constant ◽

Wind Farms ◽

Power Production ◽

Original Model ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Onshore Wind ◽

Wake Model

Abstract. In this paper, a new calculation procedure to improve the accuracy of the Jensen wake model for operating wind farms is proposed. In this procedure, the wake decay constant is updated locally at each wind turbine based on the turbulence intensity measurement provided by the nacelle anemometer. This procedure was tested against experimental data at the Sole du Moulin Vieux (SMV) onshore wind farm in France and the Horns Rev-I offshore wind farm in Denmark. Results indicate that the wake deficit at each wind turbine is described more accurately than when using the original model, reducing the error from 15 % to 20 % to approximately 5 %. Furthermore, this new model properly calibrated for the SMV wind farm is then used for coordinated control purposes. Assuming an axial induction control strategy, and following a model predictive approach, new power settings leading to an increased overall power production of the farm are derived. Power gains found are on the order of 2.5 % for a two-wind-turbine case with close spacing and 1 % to 1.5 % for a row of five wind turbines with a larger spacing. Finally, the uncertainty of the updated Jensen model is quantified considering the model inputs. When checked against the predicted power gain, the uncertainty of the model estimations is seen to be excessive, reaching approximately 4 %, which indicates the difficulty of field observations for such a gain. Nevertheless, the optimized settings are to be implemented during a field test campaign at SMV wind farm in the scope of the national project SMARTEOLE.

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Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement

Energies ◽

10.3390/en14082319 ◽

2021 ◽

Vol 14 (8) ◽

pp. 2319

Author(s):

Hyun-Goo Kim ◽

Jin-Young Kim

Keyword(s):

Power Generation ◽

Wind Speed ◽

Wind Turbine ◽

Wind Farm ◽

Free Stream ◽

Turbine Blades ◽

Wind Farms ◽

Lidar Measurement ◽

Wind Turbine Blades ◽

Wake Effect

This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated to be −0.52%p/year.

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Dynamic Modeling of an Offshore Floating Wind Turbine for Application in the Mediterranean Sea

Energies ◽

10.3390/en14010248 ◽

2021 ◽

Vol 14 (1) ◽

pp. 248

Author(s):

Lorenzo Cottura ◽

Riccardo Caradonna ◽

Alberto Ghigo ◽

Riccardo Novo ◽

Giovanni Bracco ◽

...

Keyword(s):

Wind Turbine ◽

Mediterranean Sea ◽

Wind Farm ◽

Reference Model ◽

Wide Spectrum ◽

Wind Farms ◽

Offshore Wind ◽

Floating Wind Turbine ◽

The Mediterranean ◽

Offshore Floating Wind Turbine

Wind power is emerging as one of the most sustainable and low-cost options for energy production. Far-offshore floating wind turbines are attractive in view of exploiting high wind availability sites while minimizing environmental and landscape impact. In the last few years, some offshore floating wind farms were deployed in Northern Europe for technology validation, with very promising results. At present time, however, no offshore wind farm installations have been developed in the Mediterranean Sea. The aim of this work is to comprehensively model an offshore floating wind turbine and examine the behavior resulting from a wide spectrum of sea and wind states typical of the Mediterranean Sea. The flexible and accessible in-house model developed for this purpose is compared with the reference model FAST v8.16 for verifying its reliability. Then, a simulation campaign is carried out to estimate the wind turbine LCOE (Levelized Cost of Energy). Based on this, the best substructure is chosen and the convenience of the investment is evaluated.

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