scholarly journals Wind speed variability over Iran and its impact on wind power potential: a case study for Esfehan Province

2010 ◽  
Vol 18 (2) ◽  
pp. 198-210 ◽  
Author(s):  
Fatemeh Rahimzadeh ◽  
Ali Mohammad Noorian ◽  
Mojdeh Pedram ◽  
Michael C. Kruk
Keyword(s):  
Wind Speed ◽  
Wind Power

Study on the Forecast of the Economic Loss in Storm Surge Disaster Based on Fourier Series - A Case Study of China

2013 ◽  
Vol 734-737 ◽  
pp. 3280-3285
Author(s):  
Ling Di Zhao ◽  
Ya Ru Hao
Keyword(s):  
Fourier Series ◽  
Wind Speed ◽  
Wind Power ◽  
Storm Surge ◽  
Guangdong Province ◽  
Economic Loss ◽  
Forecasting Model ◽  
Storm Surge Disaster

The economic loss forecasting model is built up on the basis of the Fourier series to simulate economic loss and grades in storm surge disaster of Zhejiang, Fujian and Guangdong Provinces. The wind speed can be used to forecast the economic loss of Guangdong Province, and the accuracy of trend and grade forecasting is good (80%). The wind power data can be used in Zhejiang and Fujian Provinces, and the accuracy results are both inferior (60%). Therefore, in the economic warning of storm surge disaster, the Fourier series model can be applied to forecast economic loss and grades.

Regional Wind Power Forecasting Based on Cloud Model and Wind Rate Vector

2013 ◽  
Vol 860-863 ◽  
pp. 405-408
Author(s):  
Dun Nan Liu ◽  
Yu Hu ◽  
Qun Li ◽  
Guang Hui Shao ◽  
Hai Ming Zhou ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Turning Point ◽  
Cloud Model ◽  
Historical Change ◽  
Power Prediction ◽  
Wind Power Forecast ◽  
Wind Rate ◽  
Rate Vector

The accuracy of wind power forecast is important to the power system operation. A new prediction model is proposed based on cloud reasoning and wind rate vector , combining with the current and the historical change rule of wind speed, using the change rule of wind speed in a period of time to forecast the power gradient in a point-in-time, The wind turbine power prediction is discussed based on power gradient and power eigenvalue. Simulation results on the case study of historical wind speed and generated power data in some area in China demonstrate that the proposed methodology can improve the accuracy of wind speed forecast and has practical value, especially for the wind turning point.

scholarly journals Qatar’s Wind Energy Potential with Associated Financial and Environmental Benefits for the Natural Gas Industry

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3329 ◽  
Author(s):  
Carlos Méndez ◽  
Yusuf Bicer
Keyword(s):  
Wind Speed ◽  
Power Plant ◽  
Natural Gas ◽  
Wind Turbine ◽  
Wind Power ◽  
Wind Farm ◽  
Oil And Gas ◽  
Environmental Benefits ◽  
Wind Power Plant

This study analyzes the possibility to use the wind’s kinetic energy to produce electricity in Northern Qatar for the natural gas processing industry. An evaluation of the wind potentiality is performed based on a thorough analysis of parameters such as wind speed and direction, temperature, atmospheric pressure, and air density. In addition, based on the measured parameters, a commercial wind turbine is selected, and a case study is presented in order to quantify the energy that a wind farm could produce and its environmental benefits. Furthermore, an economical assessment is made to quantify the repercussions that it could produce if this wind farm substitutes a fraction of the energy demand (within the oil and gas field) that is currently generated by traditional hydrocarbons. The results indicate that the environmental parameters, led by a 5.06 m/s wind speed mean, allow the production of wind energy in the area with an annual CO2 savings of 6.813 tons in a 17 MW wind power plant. This enables Qatar to reduce its internal oil and gas consumption. As a result, the amount of hydrocarbon (natural gas) saved could be used for exportation purposes, generating a positive outcome for the economy with a cost savings of about 3.32 million US$ per year through such a small size wind power plant. From the energy production point of view, the natural parameters enable a single wind turbine to produce an average of 6995.26 MWh of electricity. Furthermore, the wind farm utilized in the case study is capable of generating an average of 34.976 MWh in a year.

scholarly journals Perancangan Pembangkit Listrik Tenaga Angin Dengan Turbin Ventilator Sebagai Penggerak Generator

2017 ◽  
Vol 18 (2) ◽  
pp. 68
Author(s):  
Made Padmika ◽  
I Made Satriya Wibawa ◽  
Ni Luh Putu Trisnawati
Keyword(s):  
Wind Speed ◽  
Power Plant ◽  
Wind Power ◽  
Wind Power Plant ◽  
Maximum Output ◽  
Dc Voltage ◽  
Input And Output ◽  
Instrument Testing

A prototype of a wind power plant had been created using a ventilator  as a generator spiner. This power plant utilizes wind speed as its propulsion. Electricity generated in the DC voltage form between 0 volts up to 7.46 volts. The MT3608 module is used to stabilize and raise the voltage installed in the input and output of the charging circuit. For instrument testing, the wind speed on 0 m/s up to 6 m/s interval used. Maximum output of this tool with a wind speed of 6 m/s is 7.46 volts.

Multi-step real-time wind speed prediction based on convolution memory network

2021 ◽  
pp. 0309524X2199826
Author(s):  
Guowei Cai ◽  
Yuqing Yang ◽  
Chao Pan ◽  
Dian Wang ◽  
Fengjiao Yu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wind Speed ◽  
Real Time ◽  
Wind Power ◽  
Spatial Correlation ◽  
Prediction Accuracy ◽  
Spatial Information ◽  
Spatial Association ◽  
Spatial Feature ◽  
Time Prediction

Multi-step real-time prediction based on the spatial correlation of wind speed is a research hotspot for large-scale wind power grid integration, and this paper proposes a multi-location multi-step wind speed combination prediction method based on the spatial correlation of wind speed. The correlation coefficients were determined by gray relational analysis for each turbine in the wind farm. Based on this, timing-control spatial association optimization is used for optimization and scheduling, obtaining spatial information on the typical turbine and its neighborhood information. This spatial information is reconstructed to improve the efficiency of spatial feature extraction. The reconstructed spatio-temporal information is input into a convolutional neural network with memory cells. Spatial feature extraction and multi-step real-time prediction are carried out, avoiding the problem of missing information affecting prediction accuracy. The method is innovative in terms of both efficiency and accuracy, and the prediction accuracy and generalization ability of the proposed method is verified by predicting wind speed and wind power for different wind farms.

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