State Estimation for Power Distribution System Using Graph Neural Networks
State Estimation for Power Distribution System Using Graph Neural Networks
复制标题
DOI：
10.1109/ests56571.2023.10220523
复制
发表时间：
2023-08
期刊：
影响因子：
--
通讯作者：
Quang-Ha Ngo;Bang L. H. Nguyen;T. Vu;T. Ngo
中科院分区：
文献类型：
--
作者：
Quang-Ha Ngo;Bang L. H. Nguyen;T. Vu;T. Ngo
状态估计能够实时监测电力系统的运行情况，便于故障检测，最大限度地降低停电风险，提高系统整体性能，对维护系统的稳定性和可靠性至关重要。本文提出了一种基于图神经网络的状态估计方法，以提高时间效率和扩展可观察性。图神经网络可以聚合关键母线电压和功率测量的信息和依赖关系，使其更有效地用于非电网结构化数据的状态估计。以IEEE 123总线系统为例，对状态估计性能进行了综合评价。与其他神经网络相比，该模型在测量数据与状态的映射方面具有更好的性能。
State estimation is critical to maintaining system stability and reliability as it enables real-time monitoring of the power system operation and facilitates fault detection, minimizing the risk of power outages and improving overall system performance. This paper presents a state estimation method based on graph neural networks, aiming to improve time efficiency and extended observability. Graph neural networks can aggregate information and dependencies from voltage and power measurement at the critical buses, making them more effective for state estimation on non-grid structured data. The IEEE 123-bus system is used as a case study to evaluate comprehensively the state estimation performance. The proposed model provides a better performance for mapping measurement data with states compared to other neural networks.