Online critical unit detection and power system security control: An instance-level feature importance analysis approach

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Abstract

Rapid and accurate detection of critical units is crucial for the security control of power systems, ensuring reliable and continuous operation. Inspired by the advantages of data-driven techniques, this paper proposes an integrated deep learning framework of dynamic security assessment, critical unit detection, and security control. In the proposed framework, a black-box deep learning model is utilized to evaluate the dynamic security of power systems. Then, the predictions of the model for specific operating conditions are interpreted by instance-level feature importance analysis. Furthermore, the critical units are detected by reasonable local interpretation, and the security control scheme is extracted with a sequential adjustment strategy according to the results of interpretation. The numerical simulations on the CEPRI36 benchmark system and the IEEE 118-bus system verified that our proposed framework is fast and accurate for specific operating conditions and, thereby, is a viable approach for online security control of power systems.

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Ren, J., Wang, L., Zhang, S., Cai, Y., & Chen, J. (2021). Online critical unit detection and power system security control: An instance-level feature importance analysis approach. Applied Sciences (Switzerland), 11(12). https://doi.org/10.3390/app11125460

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