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Power System Stability Assessment Method based on GAN and GRU-Attention using Incomplete Voltage Data
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  • Xuan Deng,
  • Yufan Hu,
  • Yiyang Jia,
  • Mao Peng
Xuan Deng

Corresponding Author:[email protected]

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Yufan Hu
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Yiyang Jia
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Mao Peng
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The social economy is growing rapidly, and the power grid load demand is increasing. To maintain the stability of the power grid, it is crucial to achieve accurate and rapid power system stability assessment. In the actual operation of the power network, data loss is an unavoidable situation. However, most of the data-driven models currently used assume that the input data is complete, which has obvious limitations in real-world applications. This paper suggests an IVS-GAN model to assess power system stability using incomplete PMU measurement data with random loss. The proposed method combines the super-resolution perception technology based on Generative Adversarial Network (GAN) with a time-series signal classification model. The generator adopts a one-dimensional U-Net network and uses convolutional layers to complete and recover missing data. The discriminator adopts a new GRU-Attention architecture proposed in this paper to better extract voltage temporal variation features on key buses. The result of this paper is that the stability evaluation method outperforms other algorithms in high voltage data loss rates on the New England 10-machine 39-bus system.
17 Apr 2023Submitted to IET Generation, Transmission & Distribution
18 Apr 2023Submission Checks Completed
18 Apr 2023Assigned to Editor
21 Apr 2023Reviewer(s) Assigned
10 May 2023Review(s) Completed, Editorial Evaluation Pending
12 May 2023Editorial Decision: Revise Major
12 Jun 20231st Revision Received
13 Jun 2023Submission Checks Completed
13 Jun 2023Assigned to Editor
15 Jun 2023Reviewer(s) Assigned
26 Jun 2023Review(s) Completed, Editorial Evaluation Pending
28 Jun 2023Editorial Decision: Accept