| dc.contributor.author |
Bansal, Hari Om |
|
| dc.date.accessioned |
2023-02-13T11:06:51Z |
|
| dc.date.available |
2023-02-13T11:06:51Z |
|
| dc.date.issued |
2019-03 |
|
| dc.identifier.uri |
https://onlinelibrary.wiley.com/doi/full/10.1002/2050-7038.12004 |
|
| dc.identifier.uri |
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/9207 |
|
| dc.description.abstract |
This paper presents the design and implementation of a photovoltaic-integrated shunt active power filter (SAPF) to improve the power quality and to generate clean power. The system uses adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking and control of synchronous reference frame theory–based SAPF. Various control schemes are implemented in MATLAB and then validated in real-time using FPGA-based computation engine of OPAL-RT 4510. Control techniques built around the artificial neural network, fuzzy logic control, and ANFIS are compared for balanced and unbalanced loads on parameters like total losses with/without compensation, voltage drop, power factor, and total harmonic distortion. |
en_US |
| dc.language.iso |
en |
en_US |
| dc.publisher |
Wiley |
en_US |
| dc.subject |
EEE |
en_US |
| dc.subject |
PV-integrated |
en_US |
| dc.subject |
Adaptive Neuro-Fuzzy Inference System (ANFIS) |
en_US |
| dc.title |
Real-time implementation of adaptive PV-integrated SAPF to enhance power quality |
en_US |
| dc.type |
Article |
en_US |