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New genetic gray wolf optimizer with a random selective mutation for wind farm layout optimization.

Mauro Amaro Pinazo1

  • 1Department of Electrical Engineering, Faculty of Electrical and Electronic Engineering, National University of Engineering, Lima, Peru.

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|December 17, 2024
PubMed
Summary
This summary is machine-generated.

A new Genetic Gray Wolf Optimizer (GGWO) optimizes wind turbine placement to boost energy production. This method effectively reduces wake effects, outperforming other algorithms in simulations.

Keywords:
Annual energy productionGenetic algorithmsGenetic gray wolf optimizerRandom selective mutationWake effectWind farm layout optimization

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Area of Science:

  • Renewable Energy Engineering
  • Computational Intelligence
  • Aerodynamics

Background:

  • Wind farm energy production is significantly impacted by the wake effect, where downstream turbines experience reduced wind speeds.
  • Optimal turbine placement is crucial for maximizing overall energy output and minimizing losses due to wake interference.

Purpose of the Study:

  • To introduce a novel optimization algorithm, the Genetic Gray Wolf Optimizer (GGWO), for determining optimal wind turbine distribution.
  • To enhance wind farm efficiency by mitigating the negative impacts of the wake effect on energy generation.

Main Methods:

  • The proposed Genetic Gray Wolf Optimizer (GGWO) integrates genetic algorithm operators (crossover, mutation, Random Selective Mutation) with a hierarchical wolf pack model (Alpha, Beta, Delta, Omicron).
  • GGWO performance was evaluated against established algorithms including Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO).
  • Simulations considered variable wind conditions (speed, direction) and diverse wind farm layouts throughout the year.

Main Results:

  • The GGWO algorithm successfully identified optimal wind turbine locations, leading to improved simulation durations and increased annual energy generation.
  • GGWO demonstrated superior performance compared to GWO, ABC, and PSO algorithms in the conducted studies.
  • The algorithm showed competitive results when compared against more complex optimization techniques like ACO.

Conclusions:

  • The Genetic Gray Wolf Optimizer (GGWO) presents an effective approach for optimizing wind turbine layout to maximize energy production.
  • GGWO's hybrid nature, combining genetic operators with a social hierarchy model, enhances solution efficiency and search capabilities.
  • This novel method offers a promising solution for improving the economic viability and operational efficiency of wind farms.