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Open Access

ORIGINAL RESEARCH

Analysis and small‐signal modelling technique for support bus DC‐link of front‐end coupling inductance high step‐up single switch boost converter in low voltage renewable source

Anusak Bilsalam, Nattakorn Ketprapajun, Puntarika Insri, Hatta Sawachan, Damien Guilbert, Phatiphat Thounthong

Version of Record online: 21 December 2023

This paper presented the high output voltage conversion ratio non-isolated DC converter, which would be applied in the electricity generation system from the substitute source, to upgrade the low-voltage to the high-voltage DC, then series connected with DC–AC inverter. The presented converter had the topology development from DC boost converter with the limitation of the low-voltage conversion ratio, which was developed and corrected with the coupling inductance technique by adding the second winding L₂ to coupling with the prototype winding and diode D₂. This technique could increase the higher voltage ratio via the ratio operate (N) of both induction coils, with the operating of a single switch, at the constant frequency 60 kHz. It worked according to the pulse width modulation with the duty cycle ≤40%. This converter receives low voltage input 36 VDC provide to high voltage output 325 VDC, at the output power 125 W and the circuit efficiency equal to 92.48%, at the full load. And could maintain the output voltage by PI control under changing of output loading condition. According, it were found that the obtained close loop results from simulation and implementation results verified the proposed and design circuit. Both results agreed with the theoretical analysis.

Open Access

THEMED ARTICLE: APPLICATION OF MACHINE LEARNING TECHNIQUES IN POWER ELECTRONIC CONVERTERS

ORIGINAL RESEARCH

Detection and mitigation of false data injection attack in DC–DC synchronous boost converter: A real‐time implementation using shallow neural network model

Venkata Siva Prasad Machina, Sreedhar Madichetty, Sriranga Suprabhath Koduru, Mohan Krishna Banda, Sukumar Mishra

Version of Record online: 21 December 2023

This article proposes a neural network-based cyber attack detection and mitigation scheme to detect and mitigate false data injection attacks on the sensors. Neural networks are used for the prediction of duty; a combination of a data sampler and a binary attack detector detects the presence of an attack. Attack mitigation is performed in the final step by analysing the outputs of prediction and detection networks.

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