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http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/9207Full metadata record
| DC Field | Value | Language |
|---|---|---|
| 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 |
| Appears in Collections: | Department of Electrical and Electronics Engineering | |
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