A Study of Small Wind Turbine Generation System for Extreme Weather Conditions

2000 ◽  
pp. 2332-2335 ◽  
Author(s):  
Kazuichi Seki ◽  
Makoto Ikeda ◽  
Keita Sagara
Keyword(s):  
Wind Turbine ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Generation System ◽  
Small Wind Turbine

Corrective Solutions for the Shaft with Flange Used for Fixing the Blade on a 5 kW Wind Turbine to Withstand Extreme Weather Conditions

2014 ◽  
Vol 1029 ◽  
pp. 118-123
Author(s):  
Rodica Bădărău ◽  
Teodor Miloş ◽  
Ilare Bordeaşu ◽  
Adrian Bej
Keyword(s):  
Mechanical Strength ◽  
Wind Turbine ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Original Solution ◽  
Root Area ◽  
Materials Used ◽  
Improved Design ◽  
The Right ◽  
Technical Solutions

The paper presents a case study on the original solution of a flange shaft as part of the root area of a 5 kW wind turbine blade. There were analyzed the causes that led to the shaft breakage under wind loadings in extreme weather conditions, and consequently technical solutions have been searched in order to improve the shaft design making it more reliable as mechanical strength at extreme wind loadings. The flange shaft is a welded subassembly that keeps the blades attached to the rotor hub. The first part of the paper consists in an analysis referring the loading status, the materials used for blade manufacturing, the identification of critical areas where the breaking was initiated and also the causes for which the materials assumed and specified in the technical design and manufacturing technology failed under loading at wind gusts of about 30 m/sec. Based on this preliminary analysis, the second part of the paper presents the technical solutions which were considered in reference to the materials and the improved design concept aiming to provide the right mechanical strength necessary to withstand specific wind loadings in extreme weather conditions.

Simulation of Small Wind Turbine Generation System with Dual-Rotor Ring Winding Slotless PMSG

2012 ◽  
Vol 132 (10) ◽  
pp. 1003-1008
Author(s):  
Shohei Tokunaga ◽  
Shota Yamakura ◽  
Katsumi Kesamaru
Keyword(s):  
Wind Turbine ◽  
Generation System ◽  
Small Wind Turbine

A104 Experimental study on small wind turbine generation system under real wind condition system

2010 ◽  
Vol 2010.15 (0) ◽  
pp. 9-10
Author(s):  
Toshiyuki ASO ◽  
Katsuya IIDA ◽  
Toshiya TANAKA ◽  
Akihiro UNNO ◽  
Keisuke HAYASAKA ◽  
...  
Keyword(s):  
Experimental Study ◽  
Wind Turbine ◽  
Wind Condition ◽  
Generation System ◽  
Small Wind Turbine

scholarly journals Rain Erosion Maps for Wind Turbines Based on Geographical Locations: A Case Study in Ireland and Britain

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
K. Pugh ◽  
M. M. Stack
Keyword(s):  
Wind Turbine ◽  
Western Europe ◽  
Meteorological Data ◽  
Turbine Blades ◽  
Weather Conditions ◽  
Annual Rainfall ◽  
Wind Turbine Blades ◽  
Weather Patterns ◽  
The Uk

AbstractErosion rates of wind turbine blades are not constant, and they depend on many external factors including meteorological differences relating to global weather patterns. In order to track the degradation of the turbine blades, it is important to analyse the distribution and change in weather conditions across the country. This case study addresses rainfall in Western Europe using the UK and Ireland data to create a relationship between the erosion rate of wind turbine blades and rainfall for both countries. In order to match the appropriate erosion data to the meteorological data, 2 months of the annual rainfall were chosen, and the differences were analysed. The month of highest rain, January and month of least rain, May were selected for the study. The two variables were then combined with other data including hailstorm events and locations of wind turbine farms to create a general overview of erosion with relation to wind turbine blades.

scholarly journals Precipitation Modeling for Extreme Weather Based on Sparse Hybrid Machine Learning and Markov Chain Random Field in a Multi-Scale Subspace

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1241
Author(s):  
Ming-Hsi Lee ◽  
Yenming J. Chen
Keyword(s):  
Machine Learning ◽  
Markov Chain ◽  
Random Field ◽  
Long Range ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Prediction Algorithm ◽  
Learning Method ◽  
Multi Scale ◽  
Hybrid Machine

This paper proposes to apply a Markov chain random field conditioning method with a hybrid machine learning method to provide long-range precipitation predictions under increasingly extreme weather conditions. Existing precipitation models are limited in time-span, and long-range simulations cannot predict rainfall distribution for a specific year. This paper proposes a hybrid (ensemble) learning method to perform forecasting on a multi-scaled, conditioned functional time series over a sparse l1 space. Therefore, on the basis of this method, a long-range prediction algorithm is developed for applications, such as agriculture or construction works. Our findings show that the conditioning method and multi-scale decomposition in the parse space l1 are proved useful in resisting statistical variation due to increasingly extreme weather conditions. Because the predictions are year-specific, we verify our prediction accuracy for the year we are interested in, but not for other years.

Multi-objective resilience enhancement program in smart grids during extreme weather conditions

2021 ◽  
Vol 129 ◽  
pp. 106824
Author(s):  
Rahman Ashrafi ◽  
Meysam Amirahmadi ◽  
Mohammad Tolou-Askari ◽  
Vahid Ghods
Keyword(s):  
Smart Grids ◽  
Weather Conditions ◽  
Extreme Weather ◽  
Multi Objective
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