用于物理感知网格学习算法的时空图卷积神经网络
Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms
AI总结：
提出STGCN-DRL无模型VVC框架，利用基于潮流的GSO、时空图卷积与PPO控制智能逆变器，并在123节点不平衡系统中验证其电压稳定效果。
摘要：
本文通过基于时空图卷积网络的深度强化学习（STGCN-DRL）框架，提出了一种无模型的电压-无功控制（VVC）算法，其目标是在不平衡配电系统中控制智能逆变器。我们首先基于潮流方程确定图移位算子（GSO）。随后，我们开发了时空图卷积网络（STGCN），并测试了循环图卷积网络（RGCN）和卷积图卷积网络（CGCN）两种架构，旨在捕捉电压相量的时空相关性。STGCN层为强化学习架构的策略函数和价值函数执行特征提取任务，然后我们利用近端策略优化（PPO）在动作空间中搜索最优策略函数并近似最优价值函数。我们进一步利用电压图信号的低通特性，为策略引入一种GCN架构，其输入是抽取后的状态向量，即部分观测。在不平衡的123节点系统上进行的案例研究验证了所提方法在缓解不稳定性以及将节点电压曲线维持在理想范围内方面的优异性能。
英文摘要：
This paper proposes a model-free Volt-VAR control (VVC) algorithm via the spatio-temporal graph ConvNet-based deep reinforcement learning (STGCN-DRL) framework, whose goal is to control smart inverters in an unbalanced distribution system. We first identify the graph shift operator (GSO) based on the power flow equations. Then, we develop a spatio-temporal graph ConvNet (STGCN), testing both recurrent graph ConvNets (RGCN) and convolutional graph ConvNets (CGCN) architectures, aimed at capturing the spatiotemporal correlation of voltage phasors. The STGCN layer performs the feature extraction task for the policy function and the value function of the reinforcement learning architecture, and then we utilize the proximal policy optimization (PPO) to search the action spaces for an optimum policy function and to approximate an optimum value function. We further utilize the low-pass property of voltage graph signal to introduce an GCN architecture for the the policy whose input is a decimated state vector, i.e. a partial observation. Case studies on the unbalanced 123-bus systems validate the excellent performance of the proposed method in mitigating instabilities and maintaining nodal voltage profiles within a desirable range.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
