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Abstract:
To address the issue of insufficient harmonic measurements on the low-voltage side of distribution transformers, this study proposes a deep learning-based method for estimating harmonics on the low-voltage side of distribution transformers by combining short-term test data and long-term power data. Initially, a method combining Fisher's optimal segmentation and the derivative dynamic time warping algorithm is used to identify the harmonic-dominant users. Subsequently, a data transformation method combining variational mode decomposition and Gramian angular field is proposed to convert the power signal of harmonic-dominant users and the harmonic signal on the low-voltage side of distribution transformers into pseudo-color Gramian power images and gray Gramian harmonic images. Finally, these two types of images are input into an improved PSRGAN (pix2pix -Super-resolution generative adversarial network) model for training to learn the mapping relationship between the power data of the harmonic source user and the harmonic data on the low-voltage side of distribution transformers, enabling the generation of long-term monitoring data for harmonics on the low-voltage side of distribution transformers. The accuracy of the proposed method is validated through simulation models and real measurement cases, and the required data are easily obtainable, demonstrating the practicality of the method for engineering. © 2025 Chinese Society for Electrical Engineering. All rights reserved.
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Proceedings of the Chinese Society of Electrical Engineering
ISSN: 0258-8013
Year: 2025
Issue: 11
Volume: 45
Page: 4305-4317
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ESI Highly Cited Papers on the List: 0 Unfold All
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30 Days PV: 0
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