A Physics-Informed Action Network for Transient Stability Preventive Control

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Abstract
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.
Funding Information
  • Science & Technology Department of Sichuan Province (2021LDTD0016)
  • Fundamental Research Funds for the Central Universities (YJ2021162)

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