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author:

Wu, Yangjian (Wu, Yangjian.) [1] | Shao, Zhenguo (Shao, Zhenguo.) [2] (Scholars:邵振国) | Zhang, Yan (Zhang, Yan.) [3] | Xu, Zhibin (Xu, Zhibin.) [4]

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Abstract:

Traditional harmonic responsibility determination methods require synchronous measurement data of harmonic voltage and current, which limits their engineering applications. In view of the characteristics of existing harmonic monitoring devices, this paper proposes a method to determine the interval of multiple harmonic source responsibility based on the statistical harmonic monitoring data. First, considering the fluctuation characteristics of harmonic sources, and an interval responsibility quantization equation is established. Secondly, the Skew-Normal Mixture of Experts clustering algorithm is used to divide the harmonic operation scenario, ensuring that the fluctuation of harmonic impedance and background harmonic voltage remain within a small range for each scenario. Meanwhile, the parameters of interval responsibility quantization equation are identified utilizing the tolerance approach, and then the interval of harmonic responsibility is quantitatively evaluated. Finally, the feasibility and effectiveness of the proposed method are verified through simulation examples. © 2023 IEEE.

Keyword:

Clustering algorithms Harmonic analysis Identification (control systems)

Community:

  • [ 1 ] [Wu, Yangjian]College of Electrical Engineering and Automation, Fuzhou University Key Laboratory of Energy Digitalization, Fuzhou University, Fujian Province University, Fuzhou, China
  • [ 2 ] [Shao, Zhenguo]College of Electrical Engineering and Automation, Fuzhou University Key Laboratory of Energy Digitalization, Fuzhou University, Fujian Province University, Fuzhou, China
  • [ 3 ] [Zhang, Yan]College of Electrical Engineering and Automation, Fuzhou University Key Laboratory of Energy Digitalization, Fuzhou University, Fujian Province University, Fuzhou, China
  • [ 4 ] [Xu, Zhibin]College of Electrical Engineering and Automation, Fuzhou University Key Laboratory of Energy Digitalization, Fuzhou University, Fujian Province University, Fuzhou, China

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Year: 2023

Page: 150-155

Language: English

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ESI Highly Cited Papers on the List: 0 Unfold All

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