Proximal Policy Optimization-Based Stability Control Method for Permanent Magnet Direct-Drive Wind Power Generation System

With the increasing proportion of wind power grid connection, the stable control performance of wind turbines is crucial for the safety and stability of the power system. This paper proposes a proximal policy optimization (PPO) algorithm-based adaptive stability control framework for low voltage rid...

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Veröffentlicht in:2025 7th International Conference on Power and Energy Technology (ICPET) S. 695 - 699
Hauptverfasser: Wang, Hongxing, Wei, Shenghua, Guo, Jingmei, Guo, Xinran, Ou, Wenjun
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 04.07.2025
Schlagworte:
Deep reinforcement learning
Low voltage
low voltage ride through
Optimization
permanent magnet synchronous generator
Permanent magnets
Phase locked loops
phase-locked loop
Power system stability
proximal policy optimization
Simulation
Stability analysis
Wind power generation
Wind turbines
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Zusammenfassung:With the increasing proportion of wind power grid connection, the stable control performance of wind turbines is crucial for the safety and stability of the power system. This paper proposes a proximal policy optimization (PPO) algorithm-based adaptive stability control framework for low voltage ride-through (LVRT) in permanent magnet direct-drive wind power generation system. Considering phase-locked loop (PLL) impact on system performance, this method real-time adjusts PLL parameters to enhance LVRT. A Markov decision process (MDP) model for LVRT control is established, and reward functions for distinct fault stages are designed. The PPO algorithm solves for the adaptive control strategy. Simulation results validate the method's effectiveness across various faults, demonstrating significant improvements in system LVRT capability, adaptability and stability control capability.
DOI:10.1109/ICPET66029.2025.11160446