scholarly journals Harmonic emission study of individual wind turbines and a wind park

2013 ◽  
pp. 432-436
Author(s):  
Kai Yang ◽  
Snezana Cundeva ◽  
Math Bollen ◽  
Mats Wahlberg
Keyword(s):  
Wind Turbines ◽  
Harmonic Emission ◽  
Wind Park ◽  
Emission Study

Aggregation and Amplification of Wind-Turbine Harmonic Emission in a Wind Park

2015 ◽  
Vol 30 (2) ◽  
pp. 791-799 ◽  
Author(s):  
Kai Yang ◽  
Math H. J. Bollen ◽  
E. O. Anders Larsson
Keyword(s):  
Wind Turbine ◽  
Harmonic Emission ◽  
Wind Park

Meteorological Aspects of Wind Park Design

Green ◽  
2011 ◽  
Vol 1 (2) ◽  
Author(s):  
Stefan Emeis
Keyword(s):  
Thermal Stability ◽  
Surface Roughness ◽  
Wind Turbines ◽  
Land Surface ◽  
Solvable Model ◽  
Thermal Conditions ◽  
Offshore Wind ◽  
Momentum Balance ◽  
Optimum Distance ◽  
Wind Park

AbstractA simple consideration - based on an analytically solvable model of the momentum balance - is presented, which calculates the reduction in wind speed at hub height in an indefinitely large wind park as function of surface roughness, atmospheric thermal stability, and the mean distance between the wind turbines in this wind park. Weakest reduction occurs for a wind park erected on a rough land surface during unstable thermal stratification (minus 2%). Highest reduction occurs for an offshore wind park over a very smooth sea surface during stable thermal conditions (minus 45%). This model can be used to find the optimum distance between wind turbines in wind parks. Likewise this model calculates the wake length of entire wind parks as function of surface roughness and thermal stability. For offshore wind parks wake lengths between 10 and 30 km are found, for onshore parks this length is much less. This additional information can be used to find the optimum distance between adjacent wind parks.

scholarly journals Measurements of harmonic emission versus active power from wind turbines

2014 ◽  
Vol 108 ◽  
pp. 304-314 ◽  
Author(s):  
Kai Yang ◽  
Math H.J. Bollen ◽  
E.O. Anders Larsson ◽  
Mats Wahlberg
Keyword(s):  
Wind Turbines ◽  
Active Power ◽  
Harmonic Emission

scholarly journals Currently Known Characteristics of Bat Species Represented in Hamburg in Respect of Wind Turbine Casualties

2019 ◽  
Author(s):  
Polina Krapivnitckaia ◽  
Veit Dominik Kunz ◽  
Carolin Floeter
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Risk Estimation ◽  
Collision Risk ◽  
Body Dimensions ◽  
Literature Research ◽  
Flight Height ◽  
Wind Park ◽  
The Law ◽  
Distance Response

Bats are animals protected by the law, however many become wind turbine related casualties. To estimate the risk from wind turbines, a systematic literature research has been conducted. A total of 6 groups of bat characteristics have been chosen as relevant for the risk estimation: body dimensions, flight height, flight style and speed, foraging space and distance, response to light, and acoustical characteristics of bat calls. Their values have been presented in this paper for the 7 bat species that are represented in the wind park near Hamburg, Germany. Analyzing the values of the known bat characteristics, conclusions about the species with high collision risk possibility have been drawn. However, these conclusions have not always been supported by the statistics of carcass findings at wind parks across Germany, which raises questions, for instance about the degree of influence of certain characteristics above others, and indicates a need for further research.

scholarly journals Methodology for Calculating the Potential Maritime Wind Park Area by Taking into Account the Needs of Shipping Sector

2018 ◽  
Vol 64 ◽  
pp. 06003
Author(s):  
Rijkure Astrida
Keyword(s):  
Renewable Energy ◽  
Wind Energy ◽  
Wind Turbines ◽  
Oil And Gas ◽  
Renewable Energy Sources ◽  
Wind Farms ◽  
Offshore Wind ◽  
Energy Sources ◽  
Energy Prices ◽  
Wind Park

Renewable energy sources (wind energy, solar energy, hydroelectricity, ocean energy, geothermal energy, biomass and biofuels) are alternatives to fossil fuel that help to reduce greenhouse gas emissions, diversify energy supplies and reduce dependency on markets of unsustainable and volatile fossil fuels, particularly oil and gas. Wind energy is one of the renewable energy sources and is considered to be self-renewable as it is the result of the Sun’s activity. Using wind energy is a rapidly developing industry today, and more and more wind turbines are installed worldwide every year, land-based wind turbines being more widespread than offshore ones. In Latvia, spread of land-based wind parks is hampered by unsettled land ownership rights, while the deployment of wind parks in the sea is a new field for all Baltic States. The neighbouring countries Estonia and Lithuania have developed their own projects for offshore wind parks, therefore the topicality of the development of wind farms in the territorial waters of Latvia has also increased. Experts have proposed best options and their locations. When assessing possibilities for development of wind parks and their capacity, the following economic factors were evaluated: construction and connection costs, potential operational costs and energy prices. The aim of this study is to develop the methodology for calculating the area of a potential wind park by considering the safety distance to shipping routes and height of the wind turbines, as well as for calculating the potential capacity of a wind park.

scholarly journals Advanced Discretisation and Visualisation Methods for Performance Profiling of Wind Turbines

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6216
Author(s):  
Michiel Dhont ◽  
Elena Tsiporkova ◽  
Veselka Boeva
Keyword(s):  
Wind Turbines ◽  
Visual Analytics ◽  
Weather Conditions ◽  
Operating Mode ◽  
Performance Profiling ◽  
Output Performance ◽  
Integration Approach ◽  
Power Maps ◽  
Continuous Stream ◽  
Wind Park

Wind turbines are typically organised as a fleet in a wind park, subject to similar, but varying, environmental conditions. This makes it possible to assess and benchmark a turbine’s output performance by comparing it to the other assets in the fleet. However, such a comparison cannot be performed straightforwardly on time series production data since the performance of a wind turbine is affected by a diverse set of factors (e.g., weather conditions). All these factors also produce a continuous stream of data, which, if discretised in an appropriate fashion, might allow us to uncover relevant insights into the turbine’s operations and behaviour. In this paper, we exploit the outcome of two inherently different discretisation approaches by statistical and visual analytics. As the first discretisation method, a complex layered integration approach is used. The DNA-like outcome allows us to apply advanced visual analytics, facilitating insightful operating mode monitoring. The second discretisation approach is applying a novel circular binning approach, capitalising on the circular nature of the angular variables. The resulting bins are then used to construct circular power maps and extract prototypical profiles via non-negative matrix factorisation, enabling us to detect anomalies and perform production forecasts.

scholarly journals Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis

2021 ◽  
Vol 11 (16) ◽  
pp. 7523
Author(s):  
Mattia Beretta ◽  
Yolanda Vidal ◽  
Jose Sepulveda ◽  
Olga Porro ◽  
Jordi Cusidó
Keyword(s):  
Neural Network ◽  
Wind Turbines ◽  
Fault Prediction ◽  
Main Bearing ◽  
Bearing Fault ◽  
Detection Model ◽  
Bearing Fault Diagnosis ◽  
Normal Behavior ◽  
Wind Park ◽  
Isolation Forest

The goal of this paper is to develop, implement, and validate a methodology for wind turbines’ main bearing fault prediction based on an ensemble of an artificial neural network (normality model designed at turbine level) and an isolation forest (anomaly detection model designed at wind park level) algorithms trained only on SCADA data. The normal behavior and the anomalous samples of the wind turbines are identified and several interpretable indicators are proposed based on the predictions of these algorithms, to provide the wind park operators with understandable information with enough time to plan operations ahead and avoid unexpected costs. The stated methodology is validated in a real underproduction wind park composed by 18 wind turbines.

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