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Power Quality Improvement of Grid-Connected DC Microgrids Using Repetitive Learning-Based PLL Under Abnormal Grid Conditions.
- Source :
- IEEE Transactions on Industry Applications; Jan/Feb2018, Vol. 54 Issue 1, p82-90, 9p
- Publication Year :
- 2018
-
Abstract
- This paper proposes a repetitive learning-based phase locked loop (RLPLL) to improve power quality of the grid-connected dc microgrids under distorted grid voltage in a weak grid. The harmonic component present in grid current in a high impedance network amplifies the distortion in voltage, which often leads to instability. Since the behavior of the conventional synchronous reference frame PLL (SRF-PLL) varies, owing to the proportional-integral gains constrained to harmonic rejection bandwidth ultimately leading to a sluggish response. However, RLPLL accommodates this limitation with a comparable dynamic performance and enhanced harmonic attenuation properties. This has been achieved by using a Lyapunov-based approach for harmonic estimation, which facilitates the periodicity and boundedness of the harmonic component to obtain an adaptive learning-based update. To deal with the computational burden, this paper also provides a low-computing alternative model of the proposed strategy. The dynamic response of RLPLL along with a comparative analysis with SRF-PLL is governed by many events directly affecting the dc voltage, which is critical for the operation of dc microgrids. Its performance is validated under different scenarios in a 1-kVA field programmable gate array-based experimental setup. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00939994
- Volume :
- 54
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Industry Applications
- Publication Type :
- Academic Journal
- Accession number :
- 127409070
- Full Text :
- https://doi.org/10.1109/TIA.2017.2756866