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Near-Optimal Cable Layout Design of a Wind Farm Using Genetic Algorithm

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Proceedings of First International Conference on Mathematical Modeling and Computational Science

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1292))

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Abstract

The wind energy is the fast-growing source of electrical energy. The total new wind power installed worldwide increased by the rate of 9.6% in 2018, reaching the capacity of \(591{\text{ GW}}\). Therefore, it is very important to optimize the production efficiency in wind farms and to keep the power losses as low as possible. This can be achieved through an optimal cable layout design. This paper deals with the optimal cable routing of a power collection system in a large-scale wind farm using genetic algorithm (GA) combination with multiple traveling salesmen problem (MTSP). The main objective is to reduce the total cable length and thereby the power losses and the investment costs. To check the reliability of the suggested optimization, this methodology is carried out on existing wind farms. The obtained results show an improvement in minimizing cable costs and length using the GA.

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Author information

Authors and Affiliations

  1. Ibn Tofail University, Kenitra, Morocco

    Chakib El Mokhi & Adnane Addaim

Authors
  1. Chakib El Mokhi
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  2. Adnane Addaim
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Corresponding author

Correspondence to Chakib El Mokhi .

Editor information

Editors and Affiliations

  1. Department of Creative Technologies and Product Design, National Taipei University of Business, Taoyuan, Taiwan

    Sheng-Lung Peng

  2. Department of Mathematics, Beijing Jiaotong University, Beijing, China

    Rong-Xia Hao

  3. Department of Computer Science and Engineering, Global Institute of Management and Technology, Krishnanagar, West Bengal, India

    Souvik Pal

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El Mokhi, C., Addaim, A. (2021). Near-Optimal Cable Layout Design of a Wind Farm Using Genetic Algorithm. In: Peng, SL., Hao, RX., Pal, S. (eds) Proceedings of First International Conference on Mathematical Modeling and Computational Science. Advances in Intelligent Systems and Computing, vol 1292. Springer, Singapore. https://doi.org/10.1007/978-981-33-4389-4_5

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