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Estimation of Wind turbine’s Produced Energy in Different Regions

Jafarian, Mohammad | 2009

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 39760 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Ranjbar, Ali Mohammad
  7. Abstract:
  8. One of the most important problems in using wind energy is the estimation of wind energy potential of a region with acceptable accuracy. To use wind energy and convert it to electrical energy it is necessary to study the economical aspects of wind farm installation, and to choose an appropriate wind turbine to be installed in a region. To do such a study and to choose approperiate wind turbine, annual energy output of different wind turbines should be estimated in that region. The porpuse of this thesis is to develop new methods to estimate annual energy production of a wind turbine by using some of the parameters of wind speed pattern of a region such as wind speed average, wind speed standard deviation and etc. with acceptable accuracy. To do so, at first different phenomena that can affect the wind turbine’s energy production are investigated and conventional methods for estimating annual wind turbine’s energy production are reviewed. Then to increase the accuracy of estimation, new methods are developed using fuzzy modeling techniques and artifititial nueral networks. The performance of these proposed methods are compared using simulation results, which show that in cases where only mean value of wind speed is at hand, radial basis network and Takagi-Sugeno methods are more accurate in estimating energy production of a wind turbin. It is also shown that the availability of the value of wind speed standard deviation does not improve the accuracy of estimation of these models. However, where the values of wind speed mean square and wind speed mean cubic are at hand beside mean wind speed, the accuracy of estimation improves significantly. In the latter case, radial basis network method is proved to be the most accurate method of estimation
  9. Keywords:
  10. Wind Turbine ; Fuzzy Modeling ; Artificial Neural Network ; Annual Energy Production

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