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Neural Network-based Integrated Reactive Power Optimization Study for Power Grids Containing Large-scale Wind Power
  • +3
  • Jie Zhao,
  • chenhao wang,
  • Biao Zhao,
  • Xiao Du,
  • 怀勋 张,
  • Lei Shang
Jie Zhao
Wuhan University
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chenhao wang
Wuhan University

Corresponding Author:2023282070175@whu.edu.cn

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Biao Zhao
Dali Power Supply Bureau of Yunnan Electric Power Grid Co
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Xiao Du
Yunnan Power Grid Co Ltd Electric Power Research Institute
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怀勋 张
Wuhan University
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Lei Shang
Wuhan University
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Abstract

This paper proposes a neural network-based integrated reactive power optimisation method for power grids containing large-scale wind power, which improves the generalisation capability of the neural network by constructing a typical wind-load scenario, and solves the integrated reactive power optimisation problem for a typical wind-load scenario after it is connected to the system using the Harris Hawk Optimisation algorithm (HHO) The method is designed to reduce the computational effort and decision time of reactive power optimization by deeply fitting the mapping relationship between grid operation state and comprehensive reactive power optimization strategy through neural networks.
06 Mar 2024Submitted to IET Generation, Transmission & Distribution
21 Mar 2024Reviewer(s) Assigned
03 Apr 2024Review(s) Completed, Editorial Evaluation Pending
03 Apr 2024Editorial Decision: Revise Major
07 Apr 20241st Revision Received
11 Apr 2024Review(s) Completed, Editorial Evaluation Pending
12 Apr 2024Editorial Decision: Revise Minor
12 Apr 20242nd Revision Received
17 Apr 2024Reviewer(s) Assigned