Adaptive extremum seeking control based LCL filter resonant frequency online estimation

Yuheng Wu, Mohammad Hazzaz Mahmud, Radha Sree Krishna Moorthy, Madhu Chinthavali, Yue Zhao

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The LCL filter has been widely used in the grid-tied inverter systems. However, the resonance of the LCL filter can reduce the system stability margin and the control performance. Moreover, the grid impedance variations can lead to the drift of the resonant frequency, which can further worsen the system robustness. Thus, it is important to know the actual resonant frequency of the LCL filter. In this letter, an adaptive extremum seeking control (AESC) based estimation scheme is proposed to estimate the resonant frequency of the LCL filter online. By injecting a high-frequency (HF) signal into the inverter output voltage, the AESC scheme can identify the extremum of the LCL filter amplitude response, i.e., resonant peak. The amplitude of injection signal is adaptive based on the inverter HF response, which can address the tradeoff between the dynamic response and inverter output current quality. Most importantly, compare to other method, the proposed scheme has very low computational complexity, which minimizes the burden to the normal inverter controller operation. Stability analysis is given in this letter, and experimental studies are conducted to validate the effectiveness of the proposed scheme.

Original languageEnglish
Pages (from-to)59-64
Number of pages6
JournalIEEE Transactions on Power Electronics
Volume37
Issue number1
DOIs
StatePublished - Jan 2022

Funding

Manuscript received May 17, 2021; revised June 15, 2021 and July 17, 2021; accepted July 26, 2021. Date of publication August 4, 2021; date of current version September 16, 2021. This work was supported by the Oak Ridge National Laboratory funded through the Department of Energy—Office of Electricity’s, Transformer Resilience and Advanced Components program led by the program manager Andre Pereira. (Corresponding author: Yue Zhao.) Yuheng Wu, Mohammad Hazzaz Mahmud, and Yue Zhao are with the Power Electronic Systems Laboratory at Arkansas, University of Arkansas, Fayetteville, AR 72701 USA (e-mail: [email protected]; [email protected]; [email protected]).

Keywords

  • Adaptive extremum seeking control (AESC)
  • low computational complexity
  • resonant frequency estimation

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