基于强化学习的CANFIS控制器自适应切负荷用于频率恢复准则导向控制

To ensure modern power systems with the ability to ride through frequency excursions after a power deficit, a refined and strict frequency recovery criterion (FRC) is formulated by grid codes. According to the FRC, this paper proposes a real-time adaptive load shedding method for frequency recovery control in receiving-end power systems. A co-active neuro-fuzzy inference system (CANFIS) based load shedding controller is developed for intelligent load shedding control. The magnitude deviation and the recovery time deviation of bus frequency are extracted based on the FRC and taken as feedback signals, while the shedding amount at current shedding round and the time delay of the next round are used as control signals. To further promote the control performance of the CANFIS controller, a reinforcement learning (RL) based deterministic policy gradient (DPG) algorithm is proposed, making the frequency recovery meet the FRC with the lowest cost of load shedding and enhancing robustness. Then, a decentralized load shedding strategy is constructed by CANFIS controllers deployed at load stations. Responding to the severity of frequency condition, the strategy can adaptively determine the time, location, amount and round of load shedding in real-time to make frequency recovery satisfy the FRC. Through simulations on a provincial receiving-end power system, the effectiveness and adaptability of the proposed method are demonstrated.