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Multi-objective Optimal Operation of Cascaded Hydropower Stations Based on MOPSO with Bacteria Quorum Sensing Inspired Turbulence Mechanism

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Bio-Inspired Computing -- Theories and Applications (BIC-TA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 562))

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Abstract

Operation optimization of the cascaded hydropower stations is of great significance to utilization efficiency of water, safety and stability of the grid, and comprehensive benefits of the reservoirs. In this study, a multi-objective operation optimization model of cascaded hydropower stations is established, and a novel multi-objective particle swarm optimization (MOPSO) algorithm is proposed to deal with the optimization problem of multi-objective, multi-constraint, high-dimension, and strong-coupling. A turbulence mechanism and a circular elimination strategy, are presented to strengthen the performance of MOPSO algorithm. The result of a case study indicates that, with the proposed techniques, the pro-posed algorithm performs well on both convergence and diversity of Pareto solutions, which implies that the proposed MOPSO algorithm can be used as an effective optimization tool to handle the multi-objective operation optimization of the cascaded hydropower stations.

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Acknowledgement

This work was supported by Hubei Key Laboratory of Cascaded Hydropower Stations Operation & Control (China Three Gorges University) through grant number 2013KJX01 and 2015 Scientific Research Innovation Foundation for Postgraduate of CTGU via the Grant Number 2015CX068.

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Authors and Affiliations

  1. Hubei Key Laboratory of Cascaded Hydropower Stations Operation and Control, China Three Gorges University, Yichang, 443002, Hubei, China

    Shan Cheng

  2. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang, 443002, Hubei, China

    Shan Cheng, Xiaoyu Jiang & Wei Chen

Authors
  1. Shan Cheng
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  2. Xiaoyu Jiang
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  3. Wei Chen
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Corresponding author

Correspondence to Shan Cheng .

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Editors and Affiliations

  1. Xidian University, Xi'an, China

    Maoguo Gong

  2. Huazhong Univ. of Science and Technology, Wuhan, China

    Pan Linqiang

  3. University of Petroleum, Qingdao, China

    Song Tao

  4. University of Science and Technology of China, Hefei, China

    Ke Tang

  5. Anhui University, Hefei, China

    Xingyi Zhang

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Cheng, S., Jiang, X., Chen, W. (2015). Multi-objective Optimal Operation of Cascaded Hydropower Stations Based on MOPSO with Bacteria Quorum Sensing Inspired Turbulence Mechanism. In: Gong, M., Linqiang, P., Tao, S., Tang, K., Zhang, X. (eds) Bio-Inspired Computing -- Theories and Applications. BIC-TA 2015. Communications in Computer and Information Science, vol 562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49014-3_6

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  • DOI: https://doi.org/10.1007/978-3-662-49014-3_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49013-6

  • Online ISBN: 978-3-662-49014-3

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