Fractional-Order Particle Swarm Optimization for Sustainable Energy Management

Ningning Wang, Zhenyu Lei, Haotian Li, Tao Zheng, Ting Jin, Shangce Gao*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Sustainable energy systems, which encompass both renewable energy and energy efficiency, aim to reduce reliance on finite resources such as fossil fuels while minimizing negative impacts on the environment and promoting economic and social development. Wind energy, in particular, is an increasingly important renewable source of energy that can help combat climate change and is cost-competitive with traditional sources of electricity. To maximize energy production, optimizing wind turbine layout is crucial and involves determining the optimal placement and configuration of turbines within a given space. This article proposes a novel approach called fractional-order particle swarm optimization (FOPSO) to address this problem and demonstrates that it outperforms other state-of-the-art optimization algorithms in terms of both solution quality and power generation efficiency.

Original languageEnglish
Title of host publicationProceedings - 2023 15th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages128-132
Number of pages5
ISBN (Electronic)9798350326178
DOIs
StatePublished - 2023
Event15th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2023 - Hangzhou, China
Duration: 2023/08/262023/08/27

Publication series

NameProceedings - 2023 15th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2023

Conference

Conference15th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2023
Country/TerritoryChina
CityHangzhou
Period2023/08/262023/08/27

Keywords

  • Meta-heuristic
  • Particle Swarm Optimization
  • Renewable Energy
  • Sustainable Energy Management
  • Wake Effect
  • Wind Farm

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Control and Optimization

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