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Integrating model-driven and data-driven methods for under-frequency load shedding control
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DOI：10.1016/j.epsr.2024.111103.png)
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摘要
En 中文
With the access of a high percentage of new energy sources, power system frequency stability is challenged. Under-frequency load shedding (UFLS) is one of the primary measures to maintain frequency stability. Due to the mismatch between the amount of load shed by the traditional UFLS methods and the actual active power deficit, a new UFLS method needs to be designed. An approach utilizing a deep deterministic policy gradient (DDPG) algorithm for the problem is proposed. First, the DDPG algorithm is modified to adapt to the UFLS problem. Then, the idea of model-driven is introduced to improve the validity of the model. Thus, a novel UFLS method is proposed, which integrates data-driven and model-driven ideas. Furthermore, a structure called the dual experience pool is designed to accelerate training speed and improve stability. Based on the proposed method, a UFLS control framework is designed. Finally, the suggested methodology is validated using the IEEE-39 bus system and the Fujian power grid as test cases.
Keyword:
Under-frequency load shedding
DDPG
Dual experience pool structure
Model-assisted
期刊
IF:
4.2
论文数:
1.1W
被引数:
2.2W
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