Integrated Hybrid Converter Topology with Single DC Input and Simultaneous DC and AC Outputs using SHORFA Technique

In this paper an integrated hybrid converter topology, unique DC input as well as at same time of outputs DC and AC that use a hybrid control method. At established integrated converter, accomplish high step up voltage and implement outputs of DC as well as AC to greater reliability, switching topol...

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Bibliographic Details
Published in:International journal of electronics 2021-11, Vol.108 (11), p.1843-1870
Main Authors: Dinakar raj, S., Ezhilarasi, Angeline
Format: Article
Language:English
Subjects:
Algorithms
Control methods
Converters
DC-DC Converter
Electric potential
high step up voltage gain
Hybrid control
Hybrid Cuk Converter Inverter (HCCI)
Machine learning
Optimization
Power factor
power factor correction
Switches
Switching
Topology
Voltage
voltage spikes
voltage stress
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Summary:In this paper an integrated hybrid converter topology, unique DC input as well as at same time of outputs DC and AC that use a hybrid control method. At established integrated converter, accomplish high step up voltage and implement outputs of DC as well as AC to greater reliability, switching topology is utilised. The established hybrid strategy is joint execution of spotted hyena optimiser (SHO) and random forest algorithm (RFA), therefore, it called as SHORFA method. In proposed strategy, SHO is utilised to optimise combination of the controller parameters based on the voltage variation. In light of the optimal datasets, the RFA gives the optimal switching pulses of the driver. The RFA learning process is improved to utilise SHO algorithm at aspect of objective function as minimal error. By the established converter design, voltage stress over diodes as well as power switch is lessened. Additionally, the proposed converter directs the output voltage, power factor correction, reduces the voltage spikes over the switches and restrains the switching losses. The proposed strategy performed at worksite of MATLAB/Simulink platform compared to various existing methods.
ISSN:0020-7217
1362-3060
DOI:10.1080/00207217.2020.1870747