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<p>This study explores the challenges of transient stability management in the evolving new type power systems With the increasing penetration of renewable energy sources, the system’s fault tolerance has diminished, leading to more intricate transient stability dynamics, especially in hybrid configurations that integrate conventional and renewable generation units. Traditional methods for modeling power system dynamics struggle with modern grid complexities and require substantial computational resources for real-time analysis. This creates a major challenge for fast emergency control responses. To address these issues, this study proposes a dual-driven approach that integrates data-driven insights with model-based strategies for transient power angle control, striking a balance between operational efficiency and economic feasibility. The control framework focuses on minimizing generator tripping and load shedding while ensuring system stability is swiftly restored after severe disturbances. The methodology adopts a deep reinforcement learning paradigm, embedding neural networks with physics-based principles to capture high-order differential interactions between system states and control measures. By forecasting power angle trajectories, this approach mitigates the shortcomings of purely data-driven methods. The proposed decision-making framework not only significantly enhances the system’s ability to recover stability but also improves overall supply reliability. Simulations performed on an modified IEEE-39 bus system showcase the method’s capability and reliability in handling transient stability across systems with differing levels of complexity.</p>

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Physics-informed reinforcement learning for emergency rotor angle control in power systems considering renewable energy penetration

  • 期刊论文

  • 发表日期：

    2025年5月10日

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  • Jiemai Gao,
  • Siyuan Chen,
  • Shixiong Fan,
  • Jun Zhang,
  • Kezheng Jiang,
  • Hao Jun,
  • Wenzhong Gao

摘要

This study explores the challenges of transient stability management in the evolving new type power systems With the increasing penetration of renewable energy sources, the system’s fault tolerance has diminished, leading to more intricate transient stability dynamics, especially in hybrid configurations that integrate conventional and renewable generation units. Traditional methods for modeling power system dynamics struggle with modern grid complexities and require substantial computational resources for real-time analysis. This creates a major challenge for fast emergency control responses. To address these issues, this study proposes a dual-driven approach that integrates data-driven insights with model-based strategies for transient power angle control, striking a balance between operational efficiency and economic feasibility. The control framework focuses on minimizing generator tripping and load shedding while ensuring system stability is swiftly restored after severe disturbances. The methodology adopts a deep reinforcement learning paradigm, embedding neural networks with physics-based principles to capture high-order differential interactions between system states and control measures. By forecasting power angle trajectories, this approach mitigates the shortcomings of purely data-driven methods. The proposed decision-making framework not only significantly enhances the system’s ability to recover stability but also improves overall supply reliability. Simulations performed on an modified IEEE-39 bus system showcase the method’s capability and reliability in handling transient stability across systems with differing levels of complexity.