汪旸,董向明,张越,陈钟钟,乔咏田,杨丘帆,姜涛
WANG Yang,DONG Xiangming,ZHANG Yue,CHEN Zhongzhong,QIAO Yongtian,YANG Qiufan,JIANG Tao

• 1:国家电网有限公司华中分部调度控制中心
• 2:南瑞集团有限公司(国网电力科学研究院有限公司)
• 3:北京科东电力控制系统有限责任公司

摘要(Abstract)：

针对传统电力系统频率分析方法存在计算复杂度高、建模难度大及计算精度与效率难以平衡等问题，提出了一种基于M-BigST（改进的BigST）的电力系统频率预测方法。首先，基于块级动态图学习模块与线性空间卷积层挖掘电网拓扑结构所蕴含的空间关联特征，提取节点间的局部依赖关系并生成高维语义信息。然后，通过滑动卷积核精准捕捉系统频率在时间尺度上的局部依赖关系与短期动态特征，兼顾时序特征和空间特征构建系统频率预测模型。最后，采用某地区电网实际数据进行验证，结果表明，与其他算法相比，所提系统频率预测方法在预测准确性和鲁棒性方面具有显著优势。
To solve the problems of high computational complexity, modeling difficulty, and the challenge of balancing accuracy with efficiency in conventional power system frequency analysis methods, this paper proposes a frequency prediction method based on M-BigST(modified BigST). Firstly, a block-level dynamic graph learning module and a linear spatial convolutional layer are employed to extract spatial correlation features embedded in the power grid topology, capturing local dependencies among nodes and generating high-dimensional spatial semantic information. Then, sliding convolution kernels are used to accurately capture the local temporal dependencies and short-term dynamic characteristics of system frequency, enabling a frequency prediction model that jointly considers temporal and spatial features. Finally, actual grid operation data from a certain region are used for validation. The results show that, compared with other methods, the proposed method offers significant advantages in prediction accuracy and robustness.

关键词(KeyWords)： 电力系统频率;改进的BigST;时序预测;网络拓扑;系统频率预测
power system frequency;M-BigST;temporal prediction;network topology;system frequency prediction

Abstract:

Keywords:

基金项目(Foundation): 国家电网有限公司总部科技项目（5100-202404010A-1-1-ZN）

作者(Author): 汪旸,董向明,张越,陈钟钟,乔咏田,杨丘帆,姜涛
WANG Yang,DONG Xiangming,ZHANG Yue,CHEN Zhongzhong,QIAO Yongtian,YANG Qiufan,JIANG Tao

DOI: 10.19585/j.zjdl.202512002

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