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A Physics-Informed Action Network for Transient Stability Preventive Control
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DOI：10.1109/TPWRS.2022.3233763.png)
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摘要
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This letter proposes a physics-informed action network (PIAN) for power system transient stability preventive control (TSPC). The network firstly renders deep learning to reduce the TSPC complexity. Unlike common data-driven methods that superficially imitate control experience, TSPC is then analytically embedded into the proposed PIAN network, so that to enforce the network to learn in-depth physical patterns. The well-learned PIAN enables highly generalized real-time decisions. Comparisons with one model-based and two data-driven baselines on the IEEE 39-bus system and the IEEE 145-bus system highlight that, the proposed method enables highly reliable control decisions, and beats the others in terms of decision efficiency and generalizability.
Keyword:
Power system stability
Transient analysis
Training
Thermal stability
Mathematical models
Stability criteria
Costs
Deep learning
physics-informed action network
transient stability preventive control
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
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