Wide-area damping control for inter-area oscillation (IAO) is critical to modern power systems. The recent breakthroughs in deep learning and the broad deployment of phasor measurement units (PMU) promote the development of datadriven IAO damping controllers. In this paper, the damping control of IAOs is modeled as a Markov Decision Process (MDP) and solved by the proposed Deep Deterministic Policy Gradient (DDPG) based deep reinforcement learning (DRL) approach. The proposed approach optimizes the eigenvalue distribution of the system, which determines the IAO modes in nature. The eigenvalues are evaluated by the data-driven method called dynamic mode decomposition. For a given power system, only a subset of generators selected by participation factors needs to be controlled, alleviating the control and computing burdens. A Switching Control Strategy (SCS) is introduced to improve the transient response of IAOs. Numerical simulations of the IEEE-39 New England power grid model validate the effectiveness and advanced performance of the proposed approach as well as its robustness against communication delays. In addition, we demonstrate the transfer ability of the DRL model trained on the linearized power grid model to provide effective IAO damping control in the non-linear power grid model environment.

中文翻译：

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最优区域间振荡阻尼控制：一种具有切换控制策略的迁移深度强化学习方法

区域间振荡 (IAO) 的广域阻尼控制对现代电力系统至关重要。最近深度学习的突破和相量测量单元 (PMU) 的广泛部署促进了数据驱动的 IAO 阻尼控制器的发展。在本文中，IAO 的阻尼控制被建模为马尔可夫决策过程 (MDP)，并通过所提出的基于深度确定性策略梯度 (DDPG) 的深度强化学习 (DRL) 方法求解。所提出的方法优化了系统的特征值分布，这决定了自然界的 IAO 模式。特征值通过称为动态模式分解的数据驱动方法进行评估。对于给定的电力系统，只需要控制由参与因素选择的发电机子集，从而减轻控制和计算负担。引入了开关控制策略 (SCS) 以改善 IAO 的瞬态响应。IEEE-39 新英格兰电网模型的数值模拟验证了所提出方法的有效性和先进性能及其对通信延迟的鲁棒性。此外，我们展示了在线性化电网模型上训练的 DRL 模型的迁移能力，以在非线性电网模型环境中提供有效的 IAO 阻尼控制。