State Estimation for Power System Based on Graph Neural Network
State Estimation for Power System Based on Graph Neural Network
复制标题
基于图神经网络的电力系统状态估计
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Xuefei Liu
中科院分区：
文献类型：
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作者：
Zhaoyu Wu;Qi Wang;Xuefei Liu
电网结构越来越复杂，清洁能源在电网中的比重越来越大，这对电力系统状态估计提出了更高的要求。传统的状态估计算法仅利用监控与数据采集(SCADA)系统和广域测量系统(WAMS)的测量数据同时进行状态估计，不能有效利用广域测量系统的测量数据，时间分辨率低。因此，本文基于图神经网络模型，提出了一种全网络节点的快速状态估计方法。本文在新英格兰的57个节点上进行了模拟，生成了三个不同的数据集。算例结果表明，与传统算法相比，该方法可以有效地利用WAMS测量数据进行全网高精度、高时间分辨率的状态估计。
The structure of power grid is becoming more and more complex, and the proportion of clean energy in power grid is increasing, which puts forward higher requirements for power system state estimation. The traditional algorithm only uses the measurement data of supervisory control and data acquisition (SCADA) system and wide area measurement system (WAMS) at the same time section for state estimation, fails to make effective use of WAMS measurement data, and the time resolution is low. Therefore, based on graph neural network model, this paper proposes a fast state estimation method of nodes in the whole network. This paper simulates on 57 nodes in New England and generates three different data sets. The example results show that compared with the traditional algorithm, this method can effectively use WAMS measurement data for high-precision and high-time resolution state estimation of the whole network.