Design and Implementation of a Wind Farm Controller using Aerodynamics Estimated from LIDAR Scans of Wind Turbine Blades

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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Investigating the energy handling capability of low voltage surge arresters in a wind farm under direct lightning strikes on wind turbine blades

2014 IEEE PES General Meeting | Conference & Exposition ◽

10.1109/pesgm.2014.6938781 ◽

2014 ◽

Cited By ~ 1

Author(s):

Newman Malcolm ◽

Raj Aggarwal

Keyword(s):

Wind Turbine ◽

Wind Farm ◽

Low Voltage ◽

Turbine Blades ◽

Wind Turbine Blades ◽

Lightning Strikes ◽

Surge Arresters

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Editorial on Special Issue “Wind Turbine Power Optimization Technology”

Energies ◽

10.3390/en13071796 ◽

2020 ◽

Vol 13 (7) ◽

pp. 1796

Author(s):

Francesco Castellani ◽

Davide Astolfi

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Wind Farm ◽

Power Optimization ◽

Turbine Blades ◽

Energy Yield ◽

Optimization Approach ◽

Special Issue ◽

Wind Turbine Blades ◽

Industry Practice

This Special Issue collects innovative contributions in the field of wind turbine optimization technology. The general motivation of the present Special Issue is given by the fact that there has recently been a considerable boost of the quest for wind turbine efficiency optimization in the academia and in the wind energy practitioners communities. The optimization can be focused on technology and operation of single turbine or a group of machines within a wind farm. This perspective is evidently multi-faced and the seven papers composing this Special Issue provide a representative picture of the most ground-breaking state of the art about the subject. Wind turbine power optimization means scientific research about the design of innovative aerodynamic solutions for wind turbine blades and of wind turbine single or collective control, especially for increasing rotor size and exploitation in offshore environment. It should be noticed that some recently developed aerodynamic and control solutions have become available in the industry practice and therefore an interesting line of development is the assessment of the actual impact of optimization technology for wind turbines operating in field: this calls for non-trivial data analysis and statistical methods. The optimization approach must be 360 degrees; for this reason also offshore resource should be addressed with the most up to date technologies such as floating wind turbines, in particular as regards support structures and platforms to be employed in ocean environment. Finally, wind turbine power optimization means as well improving wind farm efficiency through innovative uses of pre-existent control techniques: this is employed, for example, for active control of wake interactions in order to maximize the energy yield and minimize the fatigue loads.

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Optimal power dispatch in wind farm with life extension of wind turbine blades as target

Journal of Renewable and Sustainable Energy ◽

10.1063/1.4808051 ◽

2013 ◽

Vol 5 (3) ◽

pp. 033115 ◽

Cited By ~ 3

Author(s):

Jinhua Zhang ◽

Yongqian Liu ◽

De Tian ◽

Gouhong Chen

Keyword(s):

Wind Turbine ◽

Wind Farm ◽

Turbine Blades ◽

Life Extension ◽

Wind Turbine Blades ◽

Optimal Power ◽

Power Dispatch

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A Binary Integer Programming Method for Optimal Wind Turbines Allocation

Clean Technologies ◽

10.3390/cleantechnol3020027 ◽

2021 ◽

Vol 3 (2) ◽

pp. 462-473

Author(s):

Nikolaos M. Manousakis ◽

Constantinos S. Psomopoulos ◽

George Ch. Ioannidis ◽

Stavros D. Kaminaris

Keyword(s):

Integer Programming ◽

Wind Turbine ◽

Wind Turbines ◽

Wind Farm ◽

Turbine Blades ◽

Wind Turbine Blades ◽

Binary Integer Programming ◽

Wake Effect ◽

Square Cells ◽

Virtual Grid

The present study introduces a Binary Integer Programming (BIP) method to minimize the number of wind turbines needed to be installed in a wind farm. The locations of wind turbines are selected in a virtual grid which is constructed considering a minimum distance between the wind turbines to avoid the wake effect. Additional equality constraints are also included to the proposed formulation to prohibit or enforce the installation of wind turbines placement at specific locations of the wind farmland. Moreover, a microscopic wind turbine placement considering the local air density is studied. To verify the efficiency of this proposal, a square site was subdivided into 25 square cells providing a virtual grid with 36 candidate placement locations. Moreover, a virtual grid with 121 vertices related with a Greek island is also tested. All simulations conducted considering the area of geographical territory, the length of wind turbine blades, as well as the capacity of each turbine.

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Diagnosis of Blade Icing Using Multiple Intelligent Algorithms

Energies ◽

10.3390/en13112975 ◽

2020 ◽

Vol 13 (11) ◽

pp. 2975

Author(s):

Xiyun Yang ◽

Tianze Ye ◽

Qile Wang ◽

Zhun Tao

Keyword(s):

Neural Network ◽

Random Forest ◽

Wind Turbine ◽

Wind Farm ◽

Turbine Blades ◽

Back Propagation ◽

Active Power ◽

K Nearest Neighbor ◽

Wind Turbine Blades ◽

Fully Connected

The icing problem of wind turbine blades in northern China has a serious impact on the normal and safe operation of the unit. In order to effectively predict the icing conditions of wind turbine blades, a deep fully connected neural network optimized by machine learning (ML) algorithms based on big data from the wind farm is proposed to diagnose the icing conditions of wind turbine blades. This study first uses the random forest model to reduce the features of the supervisory control and data acquisition (SCADA) data that affect blade icing, and then uses the K-nearest neighbor (KNN) algorithm to enhance the active power feature. The features after the random forest reduction and the active power mean square error (MSE) feature enhanced by the KNN algorithm are combined and used as the input of the fully connected neural network (FCNN) to perform and an empirical analysis for the diagnosis of blade icing. The simulation results show that the proposed model has better diagnostic accuracy than the ordinary back propagation (BP) neural network and other methods.

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Review of Wind Turbine Icing Modelling Approaches

Energies ◽

10.3390/en14165207 ◽

2021 ◽

Vol 14 (16) ◽

pp. 5207 ◽

Cited By ~ 1

Author(s):

Fahed Martini ◽

Leidy Tatiana Contreras Montoya ◽

Adrian Ilinca

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Wind Farm ◽

Low Cost ◽

Turbine Blades ◽

Experimental Tests ◽

Ice Accretion ◽

Wind Turbine Blades ◽

Additional Section ◽

Research Findings

When operating in cold climates, wind turbines are vulnerable to ice accretion. The main impact of icing on wind turbines is the power losses due to geometric deformation of the iced airfoils of the blades. Significant energy losses during the wind farm lifetime must be estimated and mitigated. Finding solutions for icing calls on several areas of knowledge. Modelling and simulation as an alternative to experimental tests are primary techniques used to account for ice accretion because of their low cost and effectiveness. Several studies have been conducted to replicate ice growth on wind turbine blades using Computational Fluid Dynamics (CFD) during the last decade. While inflight icing research is well developed and well documented, wind turbine icing is still in development and has its peculiarities. This paper surveys and discusses the models, approaches and methods used in ice accretion modelling in view of their application in wind energy while summarizing the recent research findings in Surface Roughness modelling and Droplets Trajectory modelling. An An additional section discusses research on the modelling of electro-thermal icing protection systems. This paper aims to guide researchers in wind engineering to the appropriate approaches, references and tools needed to conduct reliable icing modelling for wind turbines.

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