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1.电动汽车电网接入技术国家地方联合工程实验室(山东大学),山东省济南市 250061
2.山东大学计算机科学与技术学院,山东省青岛市 266000
3.山东大学电气工程学院,山东省济南市 250061
4.国网山东省电力公司电力科学研究院,山东省济南市 250001
杨文娴(2001—),女,硕士研究生,主要研究方向:电力系统频率安全。E-mail:2761100147@qq.com
石访(1982—),男,通信作者,博士,副教授,主要研究方向:电力系统稳定分析与控制、电力系统同步相量测量技术与应用。E-mail:shifang@sdu.edu.cn
宋雪萌(1990—),女,博士,副教授,博士生导师,主要研究方向:信息检索和社交媒体分析。E-mail:songxuemeng@ sdu.edu.cn
收稿日期:2024-08-13,
修回日期:2025-05-21,
网络出版日期:2025-06-27,
纸质出版日期:2025-09-25
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杨文娴,石访,宋雪萌等.基于独立时空特征提取与分组增强的暂态频率安全评估[J].电力系统自动化,2025,49(18):170-181.
YANG Wenxian,SHI Fang,SONG Xuemeng,et al.Transient Frequency Safety Assessment Based on Independent Spatial-temporal Feature Extraction and Grouped Enhancement[J].Automation of Electric Power Systems,2025,49(18):170-181.
杨文娴,石访,宋雪萌等.基于独立时空特征提取与分组增强的暂态频率安全评估[J].电力系统自动化,2025,49(18):170-181. DOI: 10.7500/AEPS20240813001.
YANG Wenxian,SHI Fang,SONG Xuemeng,et al.Transient Frequency Safety Assessment Based on Independent Spatial-temporal Feature Extraction and Grouped Enhancement[J].Automation of Electric Power Systems,2025,49(18):170-181. DOI: 10.7500/AEPS20240813001.
大规模新能源接入使电力系统的频率动态特性更加复杂,数据驱动的暂态频率安全评估广受关注,但仍存在特征挖掘不足、先验知识未能充分利用等局限性。为此,文中提出了基于独立时空特征提取与分组增强的暂态频率安全评估方法。首先,引入自适应时空特征提取模块,通过独立的全连接邻接矩阵,表征发电机间的关联关系,提升特征提取能力;其次,设计了基于物理先验知识的分组特征增强模块,依据系统惯性中心频率指导特征融合,促进初始输入特征的交互、增强表征能力;最后,应用多尺度卷积模块融合所有特征获取最终的系统表征,通过模型训练和参数优化实现频率安全评估。标准算例仿真结果表明,文中模型的精度和鲁棒性均优于现有数据驱动模型。
The large-scale integration of renewable energy has made the frequency dynamic characteristics of power systems more complex. The data-driven transient frequency safety assessment has attracted widespread attention
but there are still limitations such as insufficient feature mining and underutilization of prior knowledge. To this end
a method for transient frequency safety assessment based on independent spatial-temporal feature extraction and grouped enhancement is proposed. First
an adaptive spatial-temporal feature extraction module is introduced to characterize the correlations among generators through independent fully-connected adjacency matrices
thereby enhancing the capability of feature extraction. Then
a grouped feature enhancement module based on physical prior knowledge is designed
which guides the feature fusion by the frequency of system inertia center
promotes interactions of initial input features
and enhances representation capabilities. Finally
the multi-scale convolutional module is used to fuse all features to obtain the final system representation
and the frequency safety assessment is achieved through model training and parameter optimization. Simulation results of the standard case demonstrate that the accuracy and robustness of the proposed model in this paper are superior to those of existing data-driven models.
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