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The front end AC/DC converter of level-II battery chargers consists of a full bridge rectifier for conversion which leads to power losses and thermal management problem at higher loads this makes the converter performance less efficient. The proposed converter Semi-Bridgeless PFC boost converter is used to achieve high efficiency, low power losses with power factor closer to unity at higher loads. The current sharing between the semiconductor devices used in the circuit makes the converter to operate at higher loads unlike conventional topologies. This paper deals with the implementation of One-Cycle Control or Integration Reset Technique in semi-bridgeless PFC boost converter. It employs a resettable integrator which control the input current to be in phase with voltage to achieve power factor closer to unity. The advantage of this technique is that it requires only one voltage, current sensor and no multipliers which makes the control technique a cost-effective solution. The circuit simulation work is done using PSIM software.

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The successful inclusion of microgrids for enhancement of the existing power system is laden with many challenges. Increase in demand and insufficient power generation results in power shutdown in several areas which comprises non-critical loads. The preface of Distributed Generation (DG) in power system has varied implications on the existing system. The concept proposed in this paper helps in time scheduling and load scheduling of a power system based on priority setting and time setting. The load management may also be done using IoT which enables load shedding from remote areas.

The human brain is considered one of the most powerful quantum computers and combining the human brain with technology can even outperform artificial intelligence. Using a Brain-Computer Interface (BCI) system, the brain signals can be analyzed and programmed for specific tasks. This research work employs BCI technology for a medical application that gives the unfortunate paralyzed individuals the capability to interact with their surroundings solely using voluntary eye blinks. This research contributes to the existing technology to be more feasible by introducing a modular design with three physically separated components: headwear, a computer, and a wheelchair. As the signal-to-noise ratio (SNR) of the existing systems is too high to separate the eye blink artifacts from the regular EEG signal, a precise ThinkGear module is used which acquired the raw EEG signal through a single dry electrode. An embedded Bluetooth module acquires and transfers the signals wirelessly to a computer. A MATLAB program captures voluntary eye blink artifacts from the brain waves and commands the movement of a miniature wheelchair via Bluetooth. To distinguish voluntary eye blinks from involuntary eye blinks, blink strength thresholds are determined. A Graphical User Interface (GUI) designed in MATLAB displays the EEG waves in realtime and enables the user to determine the movements of the wheelchair which is specially designed to take commands from the GUI. The findings from the testing phase unveil the advantages of a modular design and the efficacy of using eye blink artifacts as the control element for brain-controlled wheelchairs. The wheelchair attained a command detection and execution accuracy of 96.4% which is an improvement from the existing systems. The work presented here gives a basic understanding of the functionality of a BCI system and provides eye blink-controlled navigation of a wheelchair for patients suffering from severe paralysis.

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