论文标题
使用环境测量的机器学习协助惯性估算
Machine Learning Assisted Inertia Estimation using Ambient Measurements
论文作者
论文摘要
随着基于转换器的可再生资源的渗透量的增加，已经将不同类型的动力学引入了电源系统。由于现代电力系统的复杂性和高阶，基于数学模型的惯性估计方法变得更加困难。本文提出了基于长期卷积神经（LRCN）网络和图形卷积神经（GCN）网络的两种新型机器学习辅助惯性估计方法。从通过相量测量单元（PMU）收集的环境测量中提取了信息性特征。然后将具有高维特征和图形信息的空间结构合并，以提高惯性估计的准确性。对IEEE 24总线系统进行了案例研究。提出的基于LRCN和GCN的惯性估计模型的精度分别为97.34％和98.15％。此外，事实证明，拟议的零生成注入总线最佳PMU放置（ZGIB-OPP）能够最大化系统可观察性，随后可以提高所有提出的惯性估计模型的性能。
With the increasing penetration of converter-based renewable resources, different types of dynamics have been introduced to the power system. Due to the complexity and high order of the modern power system, mathematical model-based inertia estimation method becomes more difficult. This paper proposes two novel machine learning assisted inertia estimation methods based on long-recurrent convolutional neural (LRCN) network and graph convolutional neural (GCN) network respectively. Informative features are extracted from ambient measurements collected through phasor measurement units (PMU). Spatial structure with high dimensional features and graphical information are then incorporated to improve the accuracy of the inertia estimation. Case studies are conducted on the IEEE 24-bus system. The proposed LRCN and GCN based inertia estimation models achieve an accuracy of 97.34% and 98.15% respectively. Furthermore, the proposed zero generation injection bus based optimal PMU placement (ZGIB-OPP) has been proved to be able to maximize the system observability, which subsequently improves the performance of all proposed inertia estimation models.
