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Improved Hadamard Decomposition and Its Application in Data Compression in New-Type Power Systems

Author

Listed:
  • Zhi Ding

    (The School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Tianyao Ji

    (The School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Mengshi Li

    (The School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

Abstract

The proliferation of renewable energy sources, flexible loads, and advanced measurement devices in new-type power systems has led to an unprecedented surge in power signal data, posing significant challenges for data management and analysis. This paper presents an improved Hadamard decomposition framework for efficient power signal compression, specifically targeting voltage and current signals which constitute foundational measurements in power systems. First, we establish theoretical guarantees for decomposition uniqueness through orthogonality and non-negativity constraints, thereby ensuring consistent and reproducible signal reconstruction, which is critical for power system applications. Second, we develop an enhanced gradient descent algorithm incorporating adaptive regularization and early stopping mechanisms, achieving superior convergence performance in optimizing the Hadamard approximation. The experimental results with simulated and field data demonstrate that the proposed scheme significantly reduces data volume while maintaining critical features in the restored data. In addition, compared with other existing compression methods, this scheme exhibits remarkable advantages in compression efficiency and reconstruction accuracy, particularly in capturing transient characteristics critical for power quality analysis.

Suggested Citation

  • Zhi Ding & Tianyao Ji & Mengshi Li, 2025. "Improved Hadamard Decomposition and Its Application in Data Compression in New-Type Power Systems," Mathematics, MDPI, vol. 13(4), pages 1-18, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:4:p:671-:d:1594052
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    References listed on IDEAS

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    1. Quan Liu & Ziling Huang & Kun Chen & Jianmin Xiao, 2025. "Efficient and Real-Time Compression Schemes of Multi-Dimensional Data from Ocean Buoys Using Golomb-Rice Coding," Mathematics, MDPI, vol. 13(3), pages 1-17, January.
    2. Mihail Senyuk & Murodbek Safaraliev & Andrey Pazderin & Olga Pichugova & Inga Zicmane & Svetlana Beryozkina, 2023. "Methodology for Power Systems’ Emergency Control Based on Deep Learning and Synchronized Measurements," Mathematics, MDPI, vol. 11(22), pages 1-30, November.
    3. Mihail Senyuk & Svetlana Beryozkina & Inga Zicmane & Murodbek Safaraliev & Viktor Klassen & Firuz Kamalov, 2025. "Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data," Mathematics, MDPI, vol. 13(2), pages 1-26, January.
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