Neural network-based integrated reactive power optimization study for power grids containing large-scale wind power

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-07-31 DOI:10.1049/gtd2.13176
Jie Zhao, Chenhao Wang, Biao Zhao, Xiao Du, Huaixun Zhang, Lei Shang
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Abstract

The high uncertainty of wind power output greatly affects the rapid reactive power optimization of power systems. This paper proposes a neural network-based comprehensive reactive power optimization method for large-scale wind power grids, effectively addressing the challenges of rapid reactive power optimization in power systems. Firstly, by constructing typical wind-power-load scenarios, the generalization ability of the neural network is improved. Then, focusing on the comprehensive reactive power optimization problem after integrating typical wind-power-load scenarios into the system, the improved Harris hawks optimization algorithm (HHO) is compared with the particle swarm optimization algorithm and traditional HHO algorithm, highlighting its advantages. Finally, HHO is utilized for solving, thereby constructing a comprehensive reactive power optimization strategy tag set. Furthermore, through deep fitting of the neural network between the power grid operating state and the comprehensive reactive power optimization strategy, the computational complexity and decision-making time of reactive power optimization are reduced.

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基于神经网络的大规模风电电网综合无功优化研究
风电出力的高度不确定性极大地影响了电力系统的快速无功优化。本文提出了一种基于神经网络的大规模风电电网无功功率综合优化方法,有效解决了电力系统无功功率快速优化的难题。首先,通过构建典型的风电负荷场景,提高了神经网络的泛化能力。然后,针对将典型风电-负荷场景纳入系统后的综合无功优化问题,将改进的哈里斯鹰优化算法(HHO)与粒子群优化算法和传统的 HHO 算法进行了比较,突出了其优势。最后,利用 HHO 进行求解,从而构建了一个全面的无功优化策略标签集。此外,通过神经网络对电网运行状态和综合无功优化策略进行深度拟合,降低了无功优化的计算复杂度和决策时间。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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