基于深度强化学习的连续特征值配置自适应状态反馈控制用于区间振荡阻尼
Deep-Reinforcement-Learning-Based Adaptive State-Feedback Control for Inter-Area Oscillation Damping with Continuous Eigenvalue Configurations
AI总结：
本文将区间振荡阻尼控制建模为MDP，提出基于DRL的自适应状态反馈控制框架，结合全局参与因子选择部分发电机和切换控制策略，在IEEE 39节点系统中验证了其优于基准方法的暂态性能与鲁棒性。
摘要：
控制跨区域的区间振荡（IAO）对现代电力系统的稳定性至关重要。深度学习的最新进展，结合相量测量单元（PMU）和发电机传感器的广泛部署，推动了数据驱动的IAO阻尼控制器的发展。本文提出了一种新颖的IAO阻尼控制框架，将该控制问题建模为马尔可夫决策过程（MDP），并通过深度强化学习（DRL）进行求解。基于DRL的控制器在具有连续特征值配置的状态空间中进行训练。为了优化控制性能和成本效益，仅选择由全局参与因子识别出的部分发电机进行控制。此外，引入了一种切换控制策略（SCS），该策略有效地将基于DRL的控制器与电力系统稳定器（PSS）集成，以提升整体性能。在IEEE 39节点新英格兰电力系统上的仿真结果表明，所提方法在暂态响应方面优于两种基准方法。在线性状态空间环境中训练的基于DRL的控制器可以直接在非线性微分代数环境中进行测试。所提方法对通信延迟的鲁棒性已得到充分研究。
英文摘要：
Controlling inter-area oscillation (IAO) across wide areas is crucial for the stability of modern power systems. Recent advances in deep learning, combined with the extensive deployment of phasor measurement units (PMUs) and generator sensors, have catalyzed the development of data-driven IAO damping controllers. In this paper, a novel IAO damping control framework is presented by modeling the control problem as a Markov Decision Process (MDP) and solving it through deep reinforcement learning (DRL). The DRL-based controller is trained in the state space with continuous eigenvalue configurations. To optimize control performance and cost-efficiency, only a subset of generators, identified by global participation factors, are selected for control. In addition, a switching control strategy (SCS) is introduced that effectively integrates the DRL-based controller with power system stabilizers (PSSs) to enhance overall performance. The simulation results on the IEEE 39-bus New England power system show that the proposed method outperforms two benchmark methods regarding the transient response. The DRL-based controller trained on the linear state-space environment can be directly tested in the nonlinear differential-algebraic environment. The robustness of the proposed method against communication delays has been thoroughly investigated.
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