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Harmonics Signal Feature Extraction Techniques: A Review

Author

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  • Minh Ly Duc

    (Faculty of Commerce, Van Lang University, 69/68 Dang Thuy Tram, Ward 13, BinhThanh District, Ho Chi Minh City 70000, Vietnam
    Department of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, 17. Listopadu 15, 708 33 Ostrava, Czech Republic)

  • Petr Bilik

    (Department of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, 17. Listopadu 15, 708 33 Ostrava, Czech Republic)

  • Radek Martinek

    (Department of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, 17. Listopadu 15, 708 33 Ostrava, Czech Republic)

Abstract

Harmonic estimation is essential for mitigating or suppressing harmonic distortions in power systems. The most important idea is that spectrum analysis, waveform estimation, harmonic source classification, source location, the determination of harmonic source contributions, data clustering, and filter-based harmonic elimination capacity are also considered. The feature extraction method is a fundamental component of the optimization that improves the effectiveness of the Harmonic Mitigation method. In this study, techniques to extract fundamental frequencies and harmonics in the frequency domain, the time domain, and the spatial domain include 67 literature reviews and an overall assessment. The combinations of signal processing with artificial intelligence (AI) techniques are also reviewed and evaluated in this study. The benefit of the feature extraction methods is that the analysis extracts the powerful basic information of the feedback signals from the sensors with the most redundancy, ensuring the highest efficiency for the next sampling process of algorithms. This study provides an overview of the fundamental frequency and harmonic extraction methods of recent years, an analysis, and a presentation of their advantages and limitations.

Suggested Citation

  • Minh Ly Duc & Petr Bilik & Radek Martinek, 2023. "Harmonics Signal Feature Extraction Techniques: A Review," Mathematics, MDPI, vol. 11(8), pages 1-36, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1877-:d:1124302
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    References listed on IDEAS

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    1. Zhenhua Li & Tinghe Hu & Ahmed Abu-Siada, 2019. "A Minimum Side-Lobe Optimization Window Function and Its Application in Harmonic Detection of an Electricity Gird," Energies, MDPI, vol. 12(13), pages 1-17, July.
    2. Abdallah El Ghaly & Mohamad Tarnini & Nazih Moubayed & Khaled Chahine, 2022. "A Filter-Less Time-Domain Method for Reference Signal Extraction in Shunt Active Power Filters," Energies, MDPI, vol. 15(15), pages 1-16, July.
    3. Radek Martinek & Jaroslav Rzidky & Rene Jaros & Petr Bilik & Martina Ladrova, 2019. "Least Mean Squares and Recursive Least Squares Algorithms for Total Harmonic Distortion Reduction Using Shunt Active Power Filter Control," Energies, MDPI, vol. 12(8), pages 1-26, April.
    4. Cheng-I Chen & Yeong-Chin Chen & Chung-Hsien Chen, 2022. "Recurrent Wavelet Fuzzy Neural Network-Based Reference Compensation Current Control Strategy for Shunt Active Power Filter," Energies, MDPI, vol. 15(22), pages 1-23, November.
    5. Héctor García & Juan Segundo & Osvaldo Rodríguez-Hernández & Rafael Campos-Amezcua & Oscar Jaramillo, 2018. "Harmonic Modelling of the Wind Turbine Induction Generator for Dynamic Analysis of Power Quality," Energies, MDPI, vol. 11(1), pages 1-19, January.
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    8. Yap Hoon & Mohd Amran Mohd Radzi & Mohd Khair Hassan & Nashiren Farzilah Mailah, 2017. "Control Algorithms of Shunt Active Power Filter for Harmonics Mitigation: A Review," Energies, MDPI, vol. 10(12), pages 1-29, December.
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