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Maximizing Energy Output of a Wind Farm Using Teaching–Learning-Based Optimization

[+] Author Affiliations
Jaydeep Patel, Vimal Savsani, Rajesh Patel

Pandit Deendayal Petroleum University, Gandhinagar, India

Paper No. ES2015-49164, pp. V002T19A004; 10 pages
  • ASME 2015 9th International Conference on Energy Sustainability collocated with the ASME 2015 Power Conference, the ASME 2015 13th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2015 Nuclear Forum
  • Volume 2: Photovoltaics; Renewable-Non-Renewable Hybrid Power System; Smart Grid, Micro-Grid Concepts; Energy Storage; Solar Chemistry; Solar Heating and Cooling; Sustainable Cities and Communities, Transportation; Symposium on Integrated/Sustainable Building Equipment and Systems; Thermofluid Analysis of Energy Systems Including Exergy and Thermoeconomics; Wind Energy Systems and Technologies
  • San Diego, California, USA, June 28–July 2, 2015
  • Conference Sponsors: Advanced Energy Systems Division, Solar Energy Division
  • ISBN: 978-0-7918-5685-7
  • Copyright © 2015 by ASME


World is facing a big problems for fossil fuel as it deals with the issues like availability, environmental effect like global warming etc, which forces us to explore new renewable sources of energy like solar, tidal, geothermal, wind etc. Among all the energies wind energy is the effective form of energy. As evaluated from the research, main cause for reduction of energy output in wind farm is the positioning of the wind turbine, as it is a function of wake loss. Present paper investigates an effective meta-heuristics optimization method known as Teaching–Learning-Based Optimization (TLBO), to optimize the positioning of the wind turbine in a wind farm. Two different scenarios of wind speed and its direction distribution across the wind site is considered like, (a) uniform wind speed of 12 m/s with uniform direction and (b) uniform wind speed of 12 m/s with variable wind direction. The results show that the implementation of TLBO is effective then other existing strategy, in terms of maximized expected power output and minimum wake effect of turbines by each other.

Copyright © 2015 by ASME



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