ABSTRACT:
This paper deals with the modified kernel least mean
square (KLMS) control strategy in double-stage, solar photovoltaic (PV) grid
tied system to enhance the power quality at common coupling point (CCP). This
proposed control algorithm has less oscillations, fast convergence, fast
dynamic response and good steady state performance. A control strategy is used
to extract the fundamental active current component of load and generates
reference grid current for a DC-AC converter. The proposed modified KLMS control
mitigates multiple power quality concerns such as harmonics reduction, unity
power factor and load balancing. The dynamic performance of proposed system is
confirmed into the MATLAB\Simulink environment. Test results on hardware
implementation are presented at varying solar irradiation levels and load
unbalancing. Test results are found satisfactory and total harmonic distortion
(THD) of the grid currents are observed well within the IEEE-519 standard.
KEYWORDS:
1.
Solar PV Generation
2.
Voltage Source Converter (VSC)
3.
Distributed Network
4.
Power Quality
SOFTWARE:
MATLAB/SIMULINK
CONCLUSION:
The
proposed modified KLMS based control scheme for double stage solar PV system,
has been simulated in MATLAB\Simulink environment and simulated results are
validated through the experimental prototype. The MPPT has extricated the peak
power point successfully (nearly 100%) from the solar PV array under varying
insolation levels. The proposed control effectively provides harmonics
compensation, grid currents balancing and unity power factor in the grid tied
system. This proposed modified KLMS control scheme has extracted the
fundamental current component efficiently. Under the load unbalancing
condition, the fundamental current component has shown faster convergence and
less oscillations than LMS and LMF controls. Moreover, it has good steady state
and dynamic performances than LMS and LMF controls. Moreover, the THD of grid
currents, is meeting the IEEE-519 standard[12].
REFERENCES:
[1] International Energy Agency, “Medium term renewable energy market report 2016,” [Online].Available http://www.iea.org
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[3] S. M. Fatemi, M. S. Shadlu and A. Talebkhah, “Comparison of Three-Point P&O and Hill Climbing Methods for Maximum Power Point Tracking in PV Systems,” 10th Int. Power Elec., Drive Sys.and Tech. Conf.(PEDSTC), Shiraz, Iran, 2019, pp. 764-768.
[4] E. H. M. Ndiaye, A. Ndiaye, M. A. Tankari and G. Lefebvre, “Adaptive Neuro-Fuzzy Inference System Application for The Identification of a Photovoltaic System and The Forecasting of Its Maximum Power Point,” 7th Int. Conf. on Renew. Ene. Rese.and App.(ICRERA), Paris, 2018, pp. 1061-1067.
[5] N. Arab, B. Kedjar, A. Javadi and K. Al-Haddad, “A Multifunctional Single-Phase Grid-Integrated Residential Solar PV Systems Based on LQR Control,” IEEE Trans. on Ind. Appl., vol. 55, no. 2, pp. 2099-2109, March-April 2019.
