基于<em>N</em>/<em>N</em>-1潮流内嵌图卷积神经网络的电网运行方式智能调整方法

doi:10.19595/j.cnki.1000-6753.tces.241728

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摘要

运行方式调整是确保电力系统安全稳定运行的关键措施之一,目前工业界仍以人工调整为主。但随着大量新能源接入和电力电子设备应用,电网规模和复杂程度不断增加,导致依靠人工经验以试凑法进行反复调整的人工调整方法面临如下严峻挑战：效率低下、理论指导欠缺。对此,本文提出了基于N/N-1潮流内嵌图卷积神经网络的电网运行方式智能调整方法。首先,以N/N-1潮流物理模型推导设计图卷积模块前向传播表达式,提出了基于N/N-1潮流内嵌的图卷积前向传播策略,高效提取了电力系统复杂拓扑特征和潮流物理特征;进而,以电力系统N/N-1状态下潮流特征作为输入/输出特征,构建了基于多层图卷积和卷积神经网络模块协同的运行方式N/N-1潮流耦合关系模型,表征N/N-1状态下的数据驱动潮流耦合关系;其次,针对N/N-1状态下潮流越限的运行方式,提出了基于N/N-1潮流耦合关系的运行方式智能对抗调整方法,以获得运行方式精准调整策略,确保其满足静态N-1安全校验;最后,在IEEE 30节点和某实际大电网341节点系统上进行算例分析,结果验证所提方法可智能调整N/N-1状态下潮流越限的运行方式至满足静态N-1校验。

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	段师琪
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关键词 ：运行方式调整, N-1安全校验, 图卷积神经网络, 潮流内嵌, 对抗过程

Abstract：

The adjustment of the operation mode is crucial for ensuring the safety and stability of the power grid, and manual adjustment is still the main method in current industry practices. However, with the integration of numerous renewable energy sources and the application of power electronic equipment, both the scale and complexity of the power grid continue to increase, resulting in the manual adjustment method that relies on manual experience to adjust repeatedly by trial-and-error method faces the following severe challenges: low efficiency, lack of theoretical guidance. Recently, some methods for operation mode adjustment have been proposed, but most of them cannot provide the transition process and intrinsic connection between the operation modes before and after the adjustment, making it difficult for the staff to explain and analyze the operation mode adjustment issues, which affects the decision-making efficiency. To solve these problems, this paper proposes an intelligent adjustment method of power grid operation mode based on N/N-1 power flow embedded graph convolutional neural network (GCNN).
Firstly, the forward propagation expression of the convolutional module of the design diagram is derived from the N/N-1 power flow physical model, and the convolutional forward propagation strategy based on N/N-1 power flow embedding is proposed to effectively extract the node and branch features of the power system in N/N-1 conditions. Then, taking power flow characteristics in N/N-1 conditions as input/output characteristics, the N/N-1 power flow coupling model based on multi-layer graph convolution and convolutional neural network module collaboration is constructed to characterize the data-driven power flow coupling relationship in N/N-1 conditions. Next, aiming at the operation mode of power flow exceeding the limit under N/N-1 conditions, an intelligent adversarial adjustment method based on N/N-1 power flow coupling relationship is proposed to accurately analyze the adjustment process before and after the adjustment and obtain the effective adjustment strategy for the operation mode, achieving the adjustment of the operation mode of power flow exceeding the limit under N/N-1 conditions to meet the N-1 verification.
The simulation results on the IEEE 30-bus system show that the accuracy of the proposed N/N-1 power flow coupling model for predicting the nodal voltage magnitude, phase angle, branch active power, and reactive power is 99.48%, 99.72%, 99.32%, and 99.81% respectively, which is higher than that of the prediction models constructed based on other neural network architectures (fully connected neural network, convolutional neural network, graph convolutional neural network), indicating that the proposed model can accurately characterize the power flow coupling relationship under N/N-1 conditions. To verify the effectiveness of the proposed intelligent adjustment method for power grid operation mode, the operation mode with power flow limit exceeding in the IEEE 30-bus system and 341-bus system in an area is adjusted, and the adjustment process before and after the adjustment can be accurately analyzed, and the effective adjustment strategy for the operation mode can be obtained. Finally, to prove the convergence of this method, the proposed method is applied to adjust 1000 operation modes with power flow limit exceeding in the two power systems, and the results show that these operation modes can be adjusted to meet the N-1 verification with fewer adjustment times.
The following conclusions can be drawn from the simulation analysis: (1) Compared with the models based on fully connected neural network, convolutional neural network and graph convolutional neural network architectures, the proposed N/N-1 power flow coupling model can more effectively and accurately characterize the power flow coupling relationship under N/N-1 conditions. (2) The proposed intelligent adversarial adjustment method can adjust the operation mode with limit exceeding to meet all static N-1 verification with fewer adjustment times and accurately analyze the adjustment process between the operation modes before and after the adjustment, and it provides the detailed basis for the staff to make the decision of operation mode adjustment.

Key words： Operation mode adjustment N-1 verification graph convolutional neural network power flow embedded adversarial process

收稿日期: 2024-10-07

PACS:

TM761

基金资助:

国家电网有限公司科技项目资助（5100-202427022A-1-1-ZN）

通讯作者: 余娟女,1980年生,博士,教授,研究方向为电力与能源经济优化运行、风险评估、深度学习等。E-mail：yujuancqu@cqu.edu.cn

作者简介: 段师琪女,2002年生,硕士研究生,研究方向为人工智能在电力系统运行方式分析中的应用、深度学习。E-mail：3179616534@qq.com

引用本文:

段师琪, 余娟, 杨知方, 陈涛, 朱晟毅. 基于N/N-1潮流内嵌图卷积神经网络的电网运行方式智能调整方法[J]. 电工技术学报, 0, (): 20241728-20241728. Duan Shiqi, Yu Juan, Yang Zhifang, Chen Tao, Zhu Shengyi. Intelligent Adjustment Method of Power Grid Operation Mode Based on N/N-1 Power Flow Embedded Graph Convolutional Neural Network. Transactions of China Electrotechnical Society, 0, (): 20241728-20241728.

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