idsession
code

17

D-2-1

Application of Remote Sensing inWind Resource Assessment: AComparative Analysis

47

D-2-2

Fabrication study on CFRP
Mr. Endo Hiroki
lightweight vertical axis wind turban

Kyushu Sangyo University

103

D-2-3

A Hybrid Method Based on Empirical
Mode Decomposition and Pipelined
Ms. Quan Long
Recurrent Neural Networks for Wind
Speed ForecastingGas Turbine Institute, Departmentof Thermal Engineering, Tsinghua
Universitytitle

Presenter name

Dr. Vilas Warudkar

Affiliation

MAULANA AZAD NATIONALINSTITUTE OF TECHNOLOGYBHOPAL MADHYA PRADESH

1/3

31-Oct. 16:15 - 18:15 [ Wind resource evaluation and prediction (2) ]Application of Remote Sensing in Wind Resource Assessment:
A Comparative Analysis
Vilas Warudkar, Siraj Ahmed Khan and Prem Kumar Chaurasiya
MAULANA AZAD NATIONAL INSTITUTE OF TECHNOLOGY BHOPAL MADHYA PRADESH

Abstract:
The successful development of wind power depends on accurate assessment of wind potential and characteristics of wind ineach location like different heights, rotor area and wind speeds in upstream and downstream. To accomplish this, it is relevant
to place emphasis on new observation and assessment methods and strategies. Wind speed and direction are sensed on several
spatial extents. Wind sensing over the ocean is usually done by satellite mounted radar or radiometers. On a smaller scale windflows are sensed with several technologies which can be divided into direct or remote sensing. Most promising application of
remote sensing techniques are sound based (SODAR) and laser light based (LIDAR) of Doppler principle. This paper outlines
the working principle and application of remote sensing instrument in wind energy assessments. It also presents thecomparative analysis of measurements campaign conducted at Kayathar, Tamil Nadu, India at a height of 60 m and 80 m. The
comparative results between traditional measuring technique (meteorological mast) and remote sensing techniques analyzed andstudied. It also outlines advantages and limitations of both approaches.

Fabrication study on CFRP lightweight vertical axis wind turban
Hiroki Endo
Kyushu Sangyo University

Abstract:
This study is fabricated CFRP lightweight vertical axis turbine to be used in the high altitude wind power generation.Thispower generation system employed high altitude wind energy with the airborne technique of a kite and a balloon.This study iscovered “tether sprocket system”, which drive generator on the ground by the tether sprocket system transports a vertical axis
turbine.Turbine demand strength and lightness in this time.So, that manufactured the turbine of chord length 100mm, diameterof 600mm, the blade turbo width 600mm, the weight 1.8kg to using CFRP and the FDM3D printer.This time show the turbine,which manufacturing process and experiment result of wind tunnel.

2/3

31-Oct. 16:15 - 18:15 [ Wind resource evaluation and prediction (2) ]A Hybrid Method Based on Empirical Mode Decomposition and Pipelined
Recurrent Neural Networks for Wind Speed Forecasting
Quan Long and Dongxiang Jiang
Gas Turbine Institute, Department of Thermal Engineering, Tsinghua Universit

Abstract:
Wind speed is an important factor for the wind farm site selection and wind power generation. In this paper, we proposed awind speed forecasting method based on empirical mode decomposition (EMD) and pipelined current neural networks (PRNN).
Wind speed is a nonlinear and non-stationary signal while it has a wealth of periodic variation elements, such as annualperiodicity, lunar periodicity, weekly periodicity and even the offshore wind speed will show the daily periodicity. The EMD
method can extract the intrinsic mode function of this kind of signal. On the other hand, the PRNN, with the characteristics offast training speed, is suitable for the strong correlation time series data. It meets the high speed variation of wind. In this study,
the wind speed is decomposed by the EMD, and then the PRNN is used to predict the variation of the modal components. We
use the combination of the two methods and have achieved the purpose of forecasting wind speed.

3/3