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Optimization of Load Sharing in Compressor Station Based on Improved Salp Swarm Algorithm

Author

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  • Jiawei Zhang

    (School of Electronic Engineering, Xi’an Shiyou University, Xi’an 710065, China
    Shaanxi Key Laboratory of Measurement and Control Technology for Oil and Gas Wells, Xi’an 710065, China)

  • Lin Li

    (School of Electronic Engineering, Xi’an Shiyou University, Xi’an 710065, China
    Shaanxi Key Laboratory of Measurement and Control Technology for Oil and Gas Wells, Xi’an 710065, China
    These authors contributed equally to this work.)

  • Qizhi Zhang

    (School of Electronic Engineering, Xi’an Shiyou University, Xi’an 710065, China
    Shaanxi Key Laboratory of Measurement and Control Technology for Oil and Gas Wells, Xi’an 710065, China
    These authors contributed equally to this work.)

  • Yanbin Wu

    (No. 1 Oil Production Plant, Changqing Oilfield Company, Yan’an 716000, China)

Abstract

In long-distance gas transmission pipelines, there are many booster compressor stations consisting of parallel compressors that provide pressure for the delivery of natural gas. So, it is economically important to optimize the operation of the booster compressor station. The booster compressor station optimization problem is a typical mixed integer nonlinear programming (MINLP) problem, and solving it accurately and stably is a challenge. In this paper, we propose an improved salp swarm algorithm based on good point set, adaptive population division and adaptive inertia weight (GASSA) to solve this problem. In GASSA, three improvement strategies are utilized to enhance the global search capability of the algorithm and help the algorithm jump out of the local optimum. We also propose a constraint handling approach. By using semi-continuous variables, we directly describe the on or off state of the compressor instead of using auxiliary binary variables to reduce the number of variables and the difficulty of solving. The effectiveness of GASSA is firstly verified using eight standard benchmark functions, and the results show that GASSA has better performance than other selected algorithms. Then, GASSA is applied to optimize the booster compressor station load distribution model and compared with some well-known meta-heuristic algorithms. The results show that GASSA outperforms other algorithms in terms of accuracy and reliability.

Suggested Citation

  • Jiawei Zhang & Lin Li & Qizhi Zhang & Yanbin Wu, 2022. "Optimization of Load Sharing in Compressor Station Based on Improved Salp Swarm Algorithm," Energies, MDPI, vol. 15(15), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5720-:d:881755
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    References listed on IDEAS

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    1. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
    2. Milosavljevic, Predrag & Marchetti, Alejandro G. & Cortinovis, Andrea & Faulwasser, Timm & Mercangöz, Mehmet & Bonvin, Dominique, 2020. "Real-time optimization of load sharing for gas compressors in the presence of uncertainty," Applied Energy, Elsevier, vol. 272(C).
    3. Vasyl Zapukhliak & Lyubomyr Poberezhny & Pavlo Maruschak & Volodymyr Grudz Jr. & Roman Stasiuk & Janette Brezinová & Anna Guzanová, 2019. "Mathematical Modeling of Unsteady Gas Transmission System Operating Conditions under Insufficient Loading," Energies, MDPI, vol. 12(7), pages 1-14, April.
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