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Harmonic analysis in integrated energy system based on compressed sensing

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  • Yang, Ting
  • Pen, Haibo
  • Wang, Dan
  • Wang, Zhaoxia

Abstract

The advent of Integrated Energy Systems enabled various distributed energy to access the system through different power electronic devices. The development of this has made the harmonic environment more complex. It needs low complexity and high precision of harmonic detection and analysis methods to improve power quality. To solve the shortages of large data storage capacities and high complexity of compression in sampling under the Nyquist sampling framework, this research paper presents a harmonic analysis scheme based on compressed sensing theory. The proposed scheme enables the performance of the functions of compressive sampling, signal reconstruction and harmonic detection simultaneously. In the proposed scheme, the sparsity of the harmonic signals in the base of the Discrete Fourier Transform (DFT) is numerically calculated first. This is followed by providing a proof of the matching satisfaction of the necessary conditions for compressed sensing. The binary sparse measurement is then leveraged to reduce the storage space in the sampling unit in the proposed scheme. In the recovery process, the scheme proposed a novel reconstruction algorithm called the Spectral Projected Gradient with Fundamental Filter (SPG-FF) algorithm to enhance the reconstruction precision. One of the actual microgrid systems is used as simulation example. The results of the experiment shows that the proposed scheme effectively enhances the precision of harmonic and inter-harmonic detection with low computing complexity, and has good capability of signal reconstruction. The maximum detection error reaches 0.0315%, and the reconstruction signals to noise ratio (SNR) is higher than 89dB.

Suggested Citation

  • Yang, Ting & Pen, Haibo & Wang, Dan & Wang, Zhaoxia, 2016. "Harmonic analysis in integrated energy system based on compressed sensing," Applied Energy, Elsevier, vol. 165(C), pages 583-591.
    • Handle: RePEc:eee:appene:v:165:y:2016:i:c:p:583-591
    DOI: 10.1016/j.apenergy.2015.12.058
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    Cited by:

    1. Wang, Xuewei & Wang, Jing & Wang, Lin & Yuan, Ruiming, 2019. "Non-overlapping moving compressive measurement algorithm for electrical energy estimation of distorted m-sequence dynamic test signal," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    2. Michal Kaczmarek & Piotr Kaczmarek & Ernest Stano, 2022. "The Effect of the Load Power Factor of the Inductive CT’s Secondary Winding on Its Distorted Current’s Harmonics Transformation Accuracy," Energies, MDPI, vol. 15(17), pages 1-11, August.
    3. Zhou, Wei & Wu, Yue & Huang, Xiang & Lu, Renzhi & Zhang, Hai-Tao, 2022. "A group sparse Bayesian learning algorithm for harmonic state estimation in power systems," Applied Energy, Elsevier, vol. 306(PB).
    4. Ernest Stano & Piotr Kaczmarek & Michal Kaczmarek, 2022. "Understanding the Frequency Characteristics of Current Error and Phase Displacement of the Corrected Inductive Current Transformer," Energies, MDPI, vol. 15(15), pages 1-16, July.
    5. Wang, Xuewei & Wang, Jing & Yuan, Ruiming & Jiang, Zhenyu, 2019. "Dynamic error testing method of electricity meters by a pseudo random distorted test signal," Applied Energy, Elsevier, vol. 249(C), pages 67-78.
    6. Michal Kaczmarek & Piotr Kaczmarek & Ernest Stano, 2022. "The Performance of the High-Current Transformer during Operation in the Wide Frequencies Range," Energies, MDPI, vol. 15(19), pages 1-15, September.
    7. Das, Laya & Garg, Dinesh & Srinivasan, Babji, 2020. "NeuralCompression: A machine learning approach to compress high frequency measurements in smart grid," Applied Energy, Elsevier, vol. 257(C).

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