DSpace logo

Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/9203
Full metadata record
DC FieldValueLanguage
dc.contributor.authorBansal, Hari Om-
dc.date.accessioned2023-02-13T09:50:56Z-
dc.date.available2023-02-13T09:50:56Z-
dc.date.issued2019-12-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0378779619302767-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/9203-
dc.description.abstractThe current power distribution system involves usage of nonlinear loads that cause power quality problems. Further, the penetration of renewable energy sources is increasing in the power networks to satisfy the consistently rising energy demand, which changes the traditional network plan and control drastically. This paper presents an intelligently controlled hybrid energy system (HES) integrated with shunt active power filter (SAPF) to address the power quality problems. Renewable sources like-Wind, PV and fuel cell (FC) are integrated into HES and are regulated using artificial intelligence techniques that are also implemented for maximum power point tracking (MPPT) in both PV and wind energy systems. The dynamic performance of SAPF is optimized using fuzzy logic, neural network and adaptive neuro-fuzzy inference system (ANFIS) based control algorithms. These controllers provide the smooth DC-link voltage and minimize the total harmonic distortion (THD) produced by the balanced/unbalanced and nonlinear loads. Comparison of these reveal that the ANFIS based algorithm provides minimum THD. The system is tested in real-time using hardware-in-the-loop (HIL) setup. The control schemes are executed on FPGA based OPAL-RT4510computational engine with microsecond step.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectEEEen_US
dc.subjectRenewable energyen_US
dc.subjectPhotovoltaicen_US
dc.subjectWind energyen_US
dc.subjectFuel cellen_US
dc.subjectMaximum Power Point Tracking (MPPT)en_US
dc.subjectAdaptive Neuro-Fuzzy Inference System (ANFIS)en_US
dc.titleInvestigations on shunt active power filter in a PV-wind-FC based hybrid renewable energy system to improve power quality using hardware-in-the-loop testing platformen_US
dc.typeArticleen_US
Appears in Collections:Department of Electrical and Electronics Engineering

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.