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<p>Facing the problem of power system data loss, this paper proposes a power system data recovery method based on a generative adversarial network. The power system clustering method utilizes aggregated hierarchical clustering and takes into consideration the similarity between different power system data. To transform the power system data recovery problem into a data generation problem, an improved GAN network data analysis method is proposed that utilizes LSTM as a generator and discriminator. Through experimental tests, the LSTM-GAN method is tested with the LSTM method, interpolation method and low-rank method to compare its effect on lost data recovery under different signals of power system data static and dynamic and four fault scenarios. The results show that the root-mean-square errors of the LSTM-GAN method for recovering data under static-dynamic fluctuations are less than 1.2%, and the difference between the errors under 55% and 15% missing data conditions is only 0.77%, with the highest data recovery error of 2.32% in the power system fault scenarios. Therefore, the GAN-based power system data recovery method can effectively realize the recovery of lost data.</p>
</abstract>

Facing the problem of power system data loss, this paper proposes a power system data recovery method based on a generative adversarial network. The power system clustering method utilizes aggregated hierarchical clustering and takes into consideration the similarity between different power system data. To transform the power system data recovery problem into a data generation problem, an improved GAN network data analysis method is proposed that utilizes LSTM as a generator and discriminator. Through experimental tests, the LSTM-GAN method is tested with the LSTM method, interpolation method and low-rank method to compare its effect on lost data recovery under different signals of power system data static and dynamic and four fault scenarios. The results show that the root-mean-square errors of the LSTM-GAN method for recovering data under static-dynamic fluctuations are less than 1.2%, and the difference between the errors under 55% and 15% missing data conditions is only 0.77%, with the highest data recovery error of 2.32% in the power system fault scenarios. Therefore, the GAN-based power system data recovery method can effectively realize the recovery of lost data.

Generative Adversarial Network-based Data Recovery Method for Power Systems

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Generative Adversarial Network-based Data Recovery Method for Power Systems"}

Facing the problem of power system data loss, this paper proposes a power system data recovery method based on a generative adversarial network. The power...

Generative Adversarial Network-based Data Recovery Method for Power Systems

Publié en ligne: 31 janv. 2024

Pages: -

Reçu: 18 déc. 2023

Accepté: 24 déc. 2023

DOI: https://doi.org/10.2478/amns-2024-0173

Mots clés
PMU measurement data, LSTM-GAN, Hierarchical clustering, Power system clustering, System data recovery

© 2024 Di Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Generative Adversarial Network-based Data Recovery Method for Power Systems

Publié en ligne: 31 janv. 2024

Pages: -

Reçu: 18 déc. 2023

Accepté: 24 déc. 2023

DOI: https://doi.org/10.2478/amns-2024-0173

Mots clésPMU measurement data, LSTM-GAN, Hierarchical clustering, Power system clustering, System data recovery

© 2024 Di Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
PMU measurement data, LSTM-GAN, Hierarchical clustering, Power system clustering, System data recovery