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Numerical data concerning wind farm layout optimization using differential evolution algorithm at different wind

Shafiq-Ur-Rehman Massan1, Asim Imdad Wagan2, Muhammad Mujtaba Shaikh3

  • 1Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Karachi, Pakistan.

Data in Brief
|October 17, 2017
PubMed
Summary
This summary is machine-generated.

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This study presents numerical data for wind turbine micrositing, generated using the differential evolution algorithm (DEA). The findings offer valuable insights into turbine efficiency and installation costs, aiding future research.

Area of Science:

  • Renewable Energy Engineering
  • Computational Optimization

Background:

  • The wind turbine micrositing problem is crucial for maximizing energy generation.
  • Existing methods often require extensive computational resources for simulation.

Purpose of the Study:

  • To provide readily usable numerical data for the wind turbine micrositing problem.
  • To facilitate direct comparison with existing and future optimization algorithms.

Main Methods:

  • Numerical data acquisition using the differential evolution algorithm (DEA).
  • Simulation performed across various wind speeds.

Main Results:

  • Data includes total dissipated power, unit turbine installation cost, and algorithm efficiency.
  • Results are presented in relation to the number of installed turbines.

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Conclusions:

  • The provided data eliminates the need for lengthy computational simulations.
  • This dataset serves as a benchmark for evaluating new micrositing algorithms.