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A group sparse Bayesian learning algorithm for harmonic state estimation in power systems

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  • Zhou, Wei
  • Wu, Yue
  • Huang, Xiang
  • Lu, Renzhi
  • Zhang, Hai-Tao

Abstract

In view of increasing concerns about climate change and global warming, various renewable energy sources are widely adopted in power systems to reduce greenhouse gas emissions. However, the widespread adoption of renewable energy sources exacerbates the problem of harmonics pollution. In order to improve power quality, it is urgent to develop a new approach for monitoring harmonics. By leveraging the structured sparsity and spatial sparsity of harmonic sources, this paper proposes a group sparse Bayesian learning method for solving harmonic state estimation problems. The proposed algorithm can employ a small number of measurements less than the number of state variables to pinpoint harmonic sources and to estimate the harmonic magnitudes and angles. Moreover, the proposed method achieves automatic hyperparameter adjustments and solves the system states in complex domain without splitting them into real and imaginary parts. Extensive results on a benchmark IEEE 14-bus system are presented to substantiate the superiority of the proposed method in terms of localization accuracy and harmonic magnitude and phase angle estimation.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:306:y:2022:i:pb:s0306261921013519
    DOI: 10.1016/j.apenergy.2021.118063
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    References listed on IDEAS

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    1. 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.
    2. Hannan, M.A. & Lipu, M.S. Hossain & Ker, Pin Jern & Begum, R.A. & Agelidis, Vasilios G. & Blaabjerg, F., 2019. "Power electronics contribution to renewable energy conversion addressing emission reduction: Applications, issues, and recommendations," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Sun, Yuanyuan & Xie, Xiangmin & Wang, Qingyan & Zhang, Linghan & Li, Yahui & Jin, Zongshuai, 2020. "A bottom-up approach to evaluate the harmonics and power of home appliances in residential areas," Applied Energy, Elsevier, vol. 259(C).
    4. Adefarati, T. & Bansal, R.C., 2019. "Reliability, economic and environmental analysis of a microgrid system in the presence of renewable energy resources," Applied Energy, Elsevier, vol. 236(C), pages 1089-1114.
    5. Bogdanov, Dmitrii & Gulagi, Ashish & Fasihi, Mahdi & Breyer, Christian, 2021. "Full energy sector transition towards 100% renewable energy supply: Integrating power, heat, transport and industry sectors including desalination," Applied Energy, Elsevier, vol. 283(C).
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    Cited by:

    1. Md Jakir Hossain & Mia Naeini, 2022. "Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters," Energies, MDPI, vol. 15(19), pages 1-17, September.
    2. Ahmadreza Eslami & Michael Negnevitsky & Evan Franklin & Sarah Lyden, 2022. "Harmonic Source Location and Characterization Based on Permissible Current Limits by Using Deep Learning and Image Processing," Energies, MDPI, vol. 15(24), pages 1-21, December.

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