Direct Lightning Hits on Wind Turbines in Winter Season Lightning Observation Results for Wind Turbines at Nikaho Wind Park in Winter

2010 ◽  
Vol 5 (1) ◽  
pp. 14-20 ◽  
Author(s):  
A. Asakawa ◽  
T. Shindo ◽  
S. Yokoyama ◽  
H. Hyodo
Keyword(s):  
Wind Turbines ◽  
Winter Season ◽  
Wind Park ◽  
Observation Results

scholarly journals Snow-pillow: creation experience and prototype operation

2020 ◽  
Vol 163 ◽  
pp. 05009
Author(s):  
Aleksandr Minnegaliev ◽  
Ruslan Rakhimov ◽  
Ruslan Suleimanov ◽  
Mansur Gainanshin
Keyword(s):  
Snow Water Equivalent ◽  
Winter Season ◽  
Operating Experience ◽  
Snow Accumulation ◽  
Analytical Models ◽  
Federal State ◽  
Digital Indicator ◽  
Instrumental Observations ◽  
Snow Water ◽  
Observation Results

A snow-pillow (or snow-measuring pillow) is a device designed to directly determine the snow water equivalent in the snow cover by measuring the weight or pressure of the snowfall. Snow-pillows allow collecting, accumulating and transmitting information about snow accumulation and snow melting with high discreteness, accuracy and promptness. Within the framework of implementing the initiated project, a snow-pillow prototype was created based on analogue strain-measuring sensors working in conjunction with an accumulating mechanism and a digital indicator run via a microcontroller. Subject to agreement with the directorate of the Federal State Budgetary Institution “Bashkir UGMS”, the snow-pillow prototype was installed on the territory of the meteorological station Ufa-Dema in November 2019. Placing the pillow at the active weather observation station allows comparing the results obtained from the prototype with the data obtained at the station. Comparing the observation results for the autumn-winter season of 2019 has allowed us to conclude that the results obtained at the snow pillow are in line with the data of instrumental observations. The operating experience of the snow pillow shows that the prototype created under the project is applicable for evaluating the snow water equivalent, provided some minor changes are introduced into the design. In the future, observation results obtained from a network of snow pillows can be used for adjusting analytical models of snow accumulation and melting within the territory.

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 Thunderstorm Characteristics during the Ontario Winter Lake-Effect Systems Project

2018 ◽  
Vol 57 (4) ◽  
pp. 853-874 ◽  
Author(s):  
Scott M. Steiger ◽  
Tyler Kranz ◽  
Theodore W. Letcher
Keyword(s):  
Boundary Layer ◽  
Wind Turbines ◽  
Winter Season ◽  
Primary Source ◽  
Lake Ontario ◽  
Field Campaign ◽  
Lake Effect ◽  
Lightning Detection ◽  
Axis Parallel ◽  
And Behavior

AbstractThe Ontario Winter Lake-Effect Systems (OWLeS) field campaign during the winter season of 2013/14 provided unprecedented data with regard to the structure and behavior of long-lake-axis-parallel (LLAP) lake-effect storms. One of the interesting characteristics of LLAP storm bands is their ability to initiate lightning. The OWLeS datasets provide an opportunity to examine more thoroughly the kinematics and microphysics of lake-effect thunder-snowstorms than ever before. The OWLeS facilities and field personnel observed six lake-effect thunderstorms during December–January 2013/14. Most of them produced very little lightning (fewer than six cloud-to-ground strokes or intracloud pulses recorded by the National Lightning Detection Network). The 7 January 2014 storm had over 50 strokes and pulses, however, which resulted in 20 flashes over a 6-h period (0630–1230 UTC), making it the most electrically active storm during the field campaign. Relative to the 18 December 2013 storm, which only had three flashes, the 7 January 2014 case had a deeper boundary layer and greater instability. Also, 45% of the lightning during the 7 January storm was likely due to flashes initiated by wind turbines or other man-made antennas, along with all of the lightning observed during 18 December. No lightning was documented over Lake Ontario, the primary source of instability for these storms.

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

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.

Optimal placement of wind turbines in a wind park using Monte Carlo simulation

2008 ◽  
Vol 33 (7) ◽  
pp. 1455-1460 ◽  
Author(s):  
Grigorios Marmidis ◽  
Stavros Lazarou ◽  
Eleftheria Pyrgioti
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Wind Turbines ◽  
Optimal Placement ◽  
Wind Park
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