Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms
Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms
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DOI：
10.1109/tsg.2023.3239740
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发表时间：
2022-03
影响因子：
9.6
通讯作者：
Tong Wu;Ignacio Losada Carreño;A. Scaglione;D. Arnold
中科院分区：
文献类型：
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作者：
Tong Wu;Ignacio Losada Carreño;A. Scaglione;D. Arnold
本文提出了用于时空图卷积和复发性神经网络的新颖体系结构，其结构受功率系统物理的启发。我们的设计背后的关键见解是从功率流程方程式得出所谓的图形移位操作员（GSO），它是图形卷积神经网络（GCN）和图形递归神经网络（GRN）设计的基石。我们证明了在两个应用中提出的体系结构的有效性：在预测电网状态以及使用深度强化学习的前瞻性电压控制方面找到随机策略。由于我们的设计可以在单相和三相不平衡的系统中采用，因此我们在两个环境中测试我们的体系结构。对于州的预测实验，我们考虑单相IEEE 118-BUS案例系统；对于电压调节，我们说明了对不平衡的三相IEEE 123-BUS馈线系统的深入增强学习政策的性能。在这两种情况下，基于物理学的GCN和GRN学习算法我们都提出优于艺术状态。
This paper proposes novel architectures for spatio-temporal graph convolutional and recurrent neural networks whose structure is inspired by the physics of power systems. The key insight behind our design consists in deriving the so-called graph shift operator (GSO), which is the cornerstone of Graph Convolutional Neural Network (GCN) and Graph Recursive Neural Network (GRN) designs, from the power flow equations. We demonstrate the effectiveness of the proposed architectures in two applications: in forecasting the power grid state and in finding a stochastic policy for foresighted voltage control using deep reinforcement learning. Since our design can be adopted in single-phase as well as three-phase unbalanced systems, we test our architecture in both environments. For state forecasting experiments we consider the single phase IEEE 118-bus case systems; for voltage regulation, we illustrate the performance of deep reinforcement learning policy on the unbalanced three-phase IEEE 123-bus feeder system. In both cases the physics based GCN and GRN learning algorithms we propose outperform the state of the art.