Abstract
The generative adversarial network based methods have been applied in many fields for simulation data generation. For power equipment, due to the combined influences of multiple factors, how to generate reasonable simulation data that meets specific requirements has become a challenge. This paper proposes a power equipment status generation approach based on generative confrontation network. This approach considers the changing factors of power equipment and takes it as the conditional distribution of simulation data during training. The proposed approach is applied to the status generation of power equipment, and the rationality and effectiveness of the approach are verified through experiments.
Supported by Technology Project of State Grid (No. 5600-201955167A-0-0-00).
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Chen, Z., Zhang, Z., Zhao, X., Guo, L. (2021). Generative Adversarial Network Based Status Generation Simulation Approach. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1451. Springer, Singapore. https://doi.org/10.1007/978-981-16-5940-9_19
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