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Advanced Hybrid Information Processing. 4th EAI International Conference, ADHIP 2020, Binzhou, China, September 26-27, 2020, Proceedings, Part II

Research Article

Intelligent Optimization Design of Reactive Voltage Sensitivity Parameters for Large-Scale Distributed Wind Farms

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  • @INPROCEEDINGS{10.1007/978-3-030-67874-6_8,
        author={Hai Hong Bian and Jian-shuo Sun and Xu Yang},
        title={Intelligent Optimization Design of Reactive Voltage Sensitivity Parameters for Large-Scale Distributed Wind Farms},
        proceedings={Advanced Hybrid Information Processing. 4th EAI International Conference, ADHIP 2020, Binzhou, China, September 26-27, 2020, Proceedings, Part II},
        proceedings_a={ADHIP PART 2},
        year={2021},
        month={1},
        keywords={Scale Decentralized Wind farm Reactive voltage Sensitivity Parameters Intelligent optimization},
        doi={10.1007/978-3-030-67874-6_8}
    }
    
  • Hai Hong Bian
    Jian-shuo Sun
    Xu Yang
    Year: 2021
    Intelligent Optimization Design of Reactive Voltage Sensitivity Parameters for Large-Scale Distributed Wind Farms
    ADHIP PART 2
    Springer
    DOI: 10.1007/978-3-030-67874-6_8
Hai Hong Bian1, Jian-shuo Sun1, Xu Yang2,*
  • 1: Nanjing Institute of Technology
  • 2: State Grid Jiangsu Electric Power Company Yangzhou Power Supply Company
*Contact email: zxg560020@sina.com

Abstract

Aiming at the problem that the reactive voltage sensitivity parameter of large-scale distributed wind farm is low overall, the parameter intelligent optimization design of the reactive voltage sensitivity of large-scale distributed wind farm is carried out. Firstly, design a wind farm equivalent circuit and optimize the parameters of the traditional reactive voltage sensitivity optimization model, and set the objective function to adjust the model weight coefficient. Then the bat algorithm is improved according to the parameter intelligent optimization model, and the reactive volt sensitivity parameter of the scaled distributed wind farm is intelligently optimized according to the improved bat algorithm. Finally, a simulation experiment is carried out to test the performance of intelligent optimization of reactive voltage sensitivity parameters of large-scale distributed wind farms. It is concluded that the reactive power sensitivity parameter of the large-scale distributed wind farm reactive voltage sensitivity parameter optimization is significantly higher than that of the reactive voltage sensitivity parameter optimized by the traditional reactive voltage sensitivity parameter optimization method.

Keywords
Scale Decentralized Wind farm Reactive voltage Sensitivity Parameters Intelligent optimization
Published
2021-01-29
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-67874-6_8
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