Journal article Open Access

Cloud-based IoT Platform DC-DC Power Converter (Non-Ideal)

Meshari J. Al Jandal; Khaled S. Al Rasheed; Muhammad R. Jamal

Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP)

This paper describes a modularized smart system architecture which is integrated with Internet of Thing (IoT) into the DC-DC converters to build a programmable technique to leverage machine learning algorithms to predict possible future faults to the system. In addition, it facilitates the performance optimization of the boost converter. This system can be established with low computing hardware to simulates the control behavior and data-driven method of IoT-based, due to the unreliability initiated from the integration of IoT technology and power electronic converters. In response to these challenges, the current paper addresses a scientific approach using small signal analysis of dc-dc boost converter (non-Ideal) with closed loop control to analyze the small deviations or abnormalities in transient region and the steady-state operating point. Complete state-space analysis is done to obtain output voltage using pulse width modulation techniques for boosting the voltage of the input voltage to a higher level by momentarily storing and release the energy in the conductor. The model of the converter is designed and simulated using voltage mode controlling method. Digital implementation based on Arduino platform was implemented to compensate perturbations of sudden load variation either on voltage or current loads. A Simulation study is conducted to validate the result of the step-up dc-dc converter using MATLAB.

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