Applying Deep Generative Methods to Generate Synthetic Data in Power Systems

Document Type : Research Paper

Authors

Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran.

Abstract

The lack of access to reliable databases, as well as the small number and imbalance of databases, is one of the main limitations of using machine learning methods in power systems, which can reduce efficiency and cause distrust in the results obtained from these methods. One of the solutions used to solve this problem is the use of Synthetic data generation. Two deep generative architectures, Generative Adversarial Network (GAN) and Variational Auto Encoder (VAE), are currently used to generate synthetic data. Due to the novelty and importance of the subject, until now, a comparative study has not been done on the research conducted in this field, in terms of subject classification, with an emphasis on validation methods of synthetic production databases. The purpose of this research is to review the studies done in this field up to now and examine the research trends for the future. In this regard, after introducing the principles of GAN and VAE deep architectures, the subject of synthetic data generation using the mentioned methods in power systems has been studied comparatively.

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References

[1] Mohsen Saffari, Mahdi Khodayar, "Spatiotemporal Deep Learning for Power System Applications: A Survey", IEEE Access, vol.12, pp.93623-93657, 2024.
[2]           M. H. Mohammadi and K. Saleh, "Synthetic benchmarks for power systems", IEEE Access, vol. 9, pp. 162706-162730, 2021.
[3] Yang Zeng, Bolin Liao, Zhan Li, Cheng Hua, Shuai Li, "A Comprehensive Review of Recent Advances on Intelligence Algorithms and Information Engineering Applications", IEEE Access, vol.12, pp.135886-135912, 2024.
[4] C. Little, et al. "Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study." arXiv preprint arXiv:2112.01925, 2021.
[5]           S. Zeng, Y. Cai, R. Zhang, and X. Lyu, "Research on Human-Machine Collaborative Aesthetic Decision-Making and Evaluation Methods in Automotive Body Design: Based on DCGAN and ANN Models," in IEEE Access, vol. 12, pp. 91575-91589, 2024, doi: 10.1109/ACCESS.2024.
[6] K. Kong, K. Kim, and S. -J. Kang, "Enhancing Stability in Training Conditional Generative Adversarial Networks via Selective Data Matching," in IEEE Access, vol. 12, pp. 119647-119659, 2024.
 
[7] C. Xu, T. Zhang, D. Zhang, D. Zhang, and J. Han, "Deep Generative Adversarial Reinforcement Learning for Semi-Supervised Segmentation of Low-Contrast and Small Objects in Medical Images," in IEEE Transactions on Medical Imaging, vol. 43, no. 9, pp. 3072-3084, Sept. 2024.
[8]           X. Zhang, Z. Zhao, R. Shao, C. Li, and H. Tang, "Mechanical Anomaly Detection and Early Warning for Ultrahigh-Voltage Shunt Reactors via Adaptive Thresholds and WGAN-GP," in IEEE Sensors Journal, vol. 24, no. 12, pp. 20219-20230, 15 June 15, 2024.
[9] Q. Zhang, X. Wang, and C. Li, "SA-WGAN-Based Optimization Method for Network Traffic Feature Camouflage," in IEEE Access, vol. 12, pp. 111142-111157, 2024.
[10]         Y. Wang, G. Sun and Q. Jin, "Imbalanced sample fault diagnosis of rotating machinery using conditional variational auto-encoder generative adversarial network", Applied Soft Computing, vol. 92, p. 106333, 2020.
[11]         A. Pinceti, L. Sankar, and O. Kosut, “Synthetic Time-Series Load Data via Conditional Generative Adversarial Networks,” 2021 IEEE Power & Energy Society General Meeting (PESGM), Jul. 2021.
[12]         X. Zheng, B. Wang, D. Kalathil, and L. Xie, “Generative Adversarial Networks-Based Synthetic PMU Data Creation for Improved Event Classification,” IEEE Open Access Journal of Power and Energy, vol. 8, pp. 68–76, 2021.
[13]         X. Gong, B. Tang, R. Zhu, W. Liao, and L. Song, "Data Augmentation for Electricity Theft Detection Using Conditional Variational Auto-Encoder", Energies, vol. 13, no. 17, p. 4291, 2020.
[14]         M. Razghandi, et al. "Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home." arXiv preprint arXiv:2201.07387,2022.
[15]         M. Razghandi, H. Zhou, M. Erol-Kantarci and D. Turgut, "Smart Home Energy Management: VAE-GAN Synthetic Dataset Generator and Q-Learning," in IEEE Transactions on Smart Grid, vol. 15, no. 2, pp. 1562-1573, March 2024.
[16]         A. Harell, R. Jones, S. Makonin and I. Bajic, "TraceGAN: Synthesizing Appliance Power Signatures Using Generative Adversarial Networks", IEEE Transactions on Smart Grid, vol. 12, no. 5, pp. 4553-4563, 2021.
[17]         J. Liu, F. Qu, X. Hong, and H. Zhang, "A Small-Sample Wind Turbine Fault Detection Method with Synthetic Fault Data Using Generative Adversarial Nets", IEEE Transactions on Industrial Informatics, vol. 15, no. 7, pp. 3877-3888, 2019.
[18]         H. Han, L. Hao, D. Cheng, and H. Xu, "GAN-SAE based fault diagnosis method for electrically driven feed pumps", PLoS ONE, vol. 15, no. 10, Oct. 2020.
[19] K. Yan, J. Su, J. Huang, and Y. Mo, "Chiller fault diagnosis based on VAE-enabled generative adversarial networks", IEEE Transactions on Automation Science and Engineering: A Publication of the IEEE Robotics and Automation Society, pp. 1-9, 2020.
[20]         F. Naaz, A. Herle, J. Channegowda, A. Raj and M. Lakshminarayanan, "A generative adversarial network‐based synthetic data augmentation technique for battery condition evaluation", International Journal of Energy Research, vol. 45, no. 13, pp. 19120-19135, 2021.
[21]         M. Udurume, C. Udeogu, AngelaC. Caliwag, and W. Lim, “Synthetic Data Generation Using GAN for RUL Prediction of Supercapacitors”, The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 3, pp. 492–500, Mar. 2022.

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History

  • Receive Date: 18 June 2024
  • Revise Date: 13 October 2024
  • Accept Date: 17 February 2025

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