Treffer: Control of grid-connected residential solar-PV system using novel adaptive linear combiner filter for power quality improvement.

Title:
Control of grid-connected residential solar-PV system using novel adaptive linear combiner filter for power quality improvement.
Authors:
Gupta, Mukesh Kumar1 (AUTHOR), Kumar, Brijesh2 (AUTHOR), Kumar, Avdhesh1 (AUTHOR) iesavd@gmail.com
Source:
Energy Sources Part A: Recovery, Utilization & Environmental Effects. Dec2025, Vol. 47 Issue 2, p1-17. 17p.
Reviews & Products:
Database:
GreenFILE

Weitere Informationen

A grid-connected residential photovoltaic (PV) system has been proposed using novel adaptive linear combiner filter-based control for power quality (PQ) improvement. In the present work, novel adaptive linear combiner (ALC) filter-based control-based control has been proposed to generate reference current. MATLAB/Simulink software is used to develop the two-stage grid-connected residential PV. The proposed control is tested in MATLAB/Simulink2018(a) under nonlinear, linear load and variable input and validated experimentally on prototype hardware. The performance of the proposed ALC filter-based control has been analyzed and compared with that of conventional synchronous d-q control. It has been observed from results that THD in grid current using proposed control is 3.74% whereas using conventional d-q control THD is 4.75%. Proposed control provides more efficient performance by improving the PQ of the system by compensating the harmonics and reactive load demand. Harmonic distortion in the grid current is within the limits as per IEEE-1547. Furthermore, proposed control has better transient response viz. settling time, under shoot and overshoot in DC link voltage during grid connection of rooftop PV system and provides, reduced complexity due to absence of phase lock loop (PLL), thus less sampling time, better accuracy, ease of implementation, and adaptability. [ABSTRACT FROM AUTHOR]

Copyright of Energy Sources Part A: Recovery, Utilization & Environmental Effects is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)