@Article{ee.2025.063165,
AUTHOR = {Qing Zhu, Denghui Guo, Rui Ruan, Zhidong Chai, Chaoqun Wang, Zhiwen Guan},
TITLE = {The Emergency Control Method for Multi-Scenario Sub-Synchronous Oscillation in Wind Power Grid Integration Systems Based on Transfer Learning},
JOURNAL = {Energy Engineering},
VOLUME = {122},
YEAR = {2025},
NUMBER = {8},
PAGES = {3133--3154},
URL = {http://www.techscience.com/energy/v122n8/63076},
ISSN = {1546-0118},
ABSTRACT = {This study presents an emergency control method for sub-synchronous oscillations in wind power grid-connected systems based on transfer learning, addressing the issue of insufficient generalization ability of traditional methods in complex real-world scenarios. By combining deep reinforcement learning with a transfer learning framework, cross-scenario knowledge transfer is achieved, significantly enhancing the adaptability of the control strategy. First, a sub-synchronous oscillation emergency control model for the wind power grid integration system is constructed under fixed scenarios based on deep reinforcement learning. A reward evaluation system based on the active power oscillation pattern of the system is proposed, introducing penalty functions for the number of machine-shedding rounds and the number of machines shed. This avoids the economic losses and grid security risks caused by the excessive one-time shedding of wind turbines. Furthermore, transfer learning is introduced into model training to enhance the model’s generalization capability in dealing with complex scenarios of actual wind power grid integration systems. By introducing the Maximum Mean Discrepancy (MMD) algorithm to calculate the distribution differences between source data and target data, the online decision-making reliability of the emergency control model is improved. Finally, the effectiveness of the proposed emergency control method for multi-scenario sub-synchronous oscillation in wind power grid integration systems based on transfer learning is analyzed using the New England 39-bus system.},
DOI = {10.32604/ee.2025.063165}
}