摘要
省级以上实际大电网节点众多,用于电网暂态稳定评估的深度学习模型输入特征空间面临维数灾,训练成本高且泛化能力难以保证,成为该类方法难以应用于实际大系统的一个瓶颈。针对这一问题,提出了一种面向稳控校核、利用网络等值进行图降维的图注意力深度学习暂态功角稳定评估模型。首先,建立全网发电机图,基于转子惯量等决定暂态功角稳定的关键参数构建其节点相似度,并利用节点相似度改进发电机图的边权重;作为一种领域知识嵌入方式,借鉴电网动态等值思路,对待研究稳控系统所涉及区域以外的网络部分,基于发电机图和分层聚类算法进行分区,将所形成分区对应成等效节点形成降维发电机图,并建立原图到降维图节点、边权重参数的映射,实现对原输入空间的降维。最后,以降维图作为输入建立图注意力深度学习模型,实现对复杂网络的暂态功角稳定评估。在南方电网某实际稳控系统算例上进行对比分析,验证了模型的有效性及准确性。
The actual large-scale power system at the provincial level or above has numerous nodes,and the input feature space of deep learning models used for transient stability assessment of power grids faces the curse of dimensionality.The high training cost and difficulty in ensuring generalization ability have become a bottleneck for the application of such methods in practical large-scale systems.A graph attention deep learning transient power angle stability evaluation model is proposed to address this issue,which is oriented towards stability control verification and utilizes network equivalence for graph dimensionality reduction.Firstly,a whole grid generators diagram is established,and node-similarity is constructed based on key parameters such as rotor inertias that determine transient power angle stability,and node similarity is used to improve the edge weights of the generator diagram.As a domain knowledge embedding method,the dynamic equivalence approach of the power grid is used for reference,the network parts outside the area involved in the study of stability control systems are partitioned based on generators diagrams and hierarchical clustering algorithms.The formed partitions are corresponding to equivalent nodes to form a reduced dimensional generators diagram,and a mapping of the original graph to the reduced dimensional graph nodes and edge weight parameters is established to achieve dimensionality reduction of the original input space.Finally,a graph attention deep learning model is established using the dimensionality reduction graph as input to achieve transient power angle stability evaluation of complex networks.The effectiveness and accuracy of the model are verified through comparative analysis on a practical stability control system example in China Southern Power Grid.
作者
张建新
蔡锱涵
李诗旸
高琴
付超
杨欢欢
杨荣照
邱建
ZHANG Jianxin;CAI Zihan;LI Shiyang;GAO Qin;FU Chao;YANG Huanhuan;YANG Rongzhao;QIU Jian(Power Dispatching and Control Center,CSG,Guangzhou 510663,China;Electric Power Research Institute,CSG,Guangzhou 510663,China)
出处
《南方电网技术》
CSCD
北大核心
2024年第4期30-40,共11页
Southern Power System Technology
基金
国家自然科学基金企业创新发展联合基金集成项目(U22B6007)
中国南方电网有限责任公司重点科技项目(ZDKJXM20190031)。
关键词
图深度学习
图降维
网络等值
暂态功角稳定评估
仿真计算
graph deep learning
graph dimensionality reduction
network equivalence
transient power angle stability evaluation
simulation calculation
