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SeqESR-GAN-Based Sparse Data Augmentation for Distribution Networks
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DOI：10.1109/TII.2024.3431009.png)
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Abstract
En 中文
The increasing integration of distributed energy resources brings considerable uncertainties to power distribution systems. Limited redundancy in measurement not only constrains dynamic state estimation but also inevitably overlooks the electricity generation/utilization characteristics of typical prosumers. To enhance the system observability, a three-stage sparse data augmentation framework, namely sequence-to-sequence enhanced super-resolution generative adversarial network (GAN) is established. A novel data image encoding method is proposed to reflect the periodic electricity utilization patterns of renewable energy sources, loads, and energy storage systems, and enhances measurement data resolution by learning energy flow behaviors. Different learning strategies are employed in the initial two stages, enabling the GAN-based model to capture the spatiotemporal characteristics of the energy tensor and restore high-frequency elements. The third stage involves a gated recurrent unit-based Seq2Seq model to eliminate time lags and invalid details in the super-resolved data. Case studies and analyses are carried out to validate the effectiveness of the proposed approach.
Keywords:
Superresolution
Image reconstruction
Tensors
Generative adversarial networks
Power systems
Electricity
Data augmentation
Distribution network
generative adversarial network (GAN)
sequence-to-sequence
super-resolution reconstruction
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9.9
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8.6K
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