Encoding Time Series as Images: A Robust and Transferable Framework for Power System DIM Identification Combining Rules and VGGNet

Source code of Encoding Time Series as Images: A Robust and Transferable Framework for Power System DIM Identification Combining Rules and VGGNet Our work is an application in the field of power system simulation data analysis. Specifically, to identify the dominant instability mode of power systems, especially distuighing the transient instability and short-term voltage instability. For more details about the problem, you can read our published papers on IJEPES and TPWRS which are presented blow.

论文Encoding Time Series as Images: A Robust and Transferable Framework for Power System DIM Identification Combining Rules and VGGNet的源码。针对电力系统主导失稳模式识别问题提出的图像识别方法，欢迎阅读我们在IJEPES, TPWRS和中国电机工程学报中的论文。

If this work is helpful with your work, welcome to cite the following papers.

@article{Zhang2022UTL,
  title={Towards Multi-Scenario Power System Stability Analysis: An Unsupervised Transfer Learning Method Combining DGAT and Data Augmentation},
  author={R. Zhang, W. Yao, Z. Shi, et al.},
  journal={IEEE Transactions on Power Systems},
  year={2023}
}

@article{Zhang2022DCVGG,
  title={Encoding Time Series as Images: A Robust and Transferable Framework for Power System DIM Identification Combining Rules and VGGNet},
  author={R. Zhang, W. Yao, Z. Shi, et al.},
  journal={IEEE Transactions on Power Systems},
  year={2023}
}

@article{Zhang2021DIM,
  title={A Graph Attention Networks-Based Model to Distinguish the Transient Rotor Angle Instability and Short-term Voltage Instability in Power Systems},
  author={R. Zhang, W. Yao, Z. Shi, et al.},
  journal={International Journal of Electrical Power and Energy Systems},
  year={2022}
}

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2023/1/4

• This paper is online available by IEEE Xplore.

2022/12/31 Updata

• This paper is successfully accepted by TPWRS.

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