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Inverters are an essential part in many applications including photovoltaic generation. With the increasing penetration of renewable energy sources, the drive for efficient inverters is gaining more and more momentum. In this paper, output power quality, power loss, implementation complexity, cost, and relative advantages of the popular cascaded multilevel H-bridge inverter and a modified version of it are explored. An optimal number of levels and the optimal switching frequency for such inverters are investigated, and a five-level architecture is chosen considering the trade-offs. This inverter is driven by level shifted in-phase disposition pulse width modulation technique to reduce harmonics, which is chosen through deliberate testing of other advanced disposition pulse width modulation techniques. To reduce the harmonics further, the application of filters is investigated, and an LC filter is applied which provided appreciable results. This system is tested in MATLAB/Simulink and then implemented in hardware after design and testing in Proteus ISIS. The general cascaded multilevel H-bridge inverter design is also implemented in hardware to demonstrate a novel low-cost MOSFET driver build for this study. The hardware setups use MOSFETs as switching devices and low-cost ATmega microcontrollers for generating the switching pulses via level shifted in-phase disposition pulse width modulation. This implementation substantiated the effectiveness of the proposed design.

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DyFe0.1Cr0.9O3 nanoparticles calcined at 700 °C demonstrate superior photocatalytic ability compared to that of DyCrO3 nanoparticles calcined at the same temperature.

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Using a single-point sensor, non-intrusive load monitoring (NILM) discerns the individual electrical appliances of a residential or commercial building by disaggregating the accumulated energy consumption data without accessing to the individual components. To classify devices, potential features need to be extracted from the electrical signatures. In this article, a novel features extraction method based on current shapelets is proposed. Time-series current shapelets are determined from the normalized current data recorded from different devices. In general, shapelets can be defined as the subsequences constituting the most distinguished shapes of a time-series sequence from a particular class and can be used to discern the class among many subsequences from different classes. In this work, current envelopes are determined from the original current data by locating and connecting the peak points for each sample. Then, a unique approach is proposed to extract shapelets from the starting phase (device is turned on) of the time-series current envelopes. Subsequences windowed from the starting moment to a few seconds of stable device operation are taken into account. Based on these shapelets, a multi-class classification model consisting of five different supervised algorithms is developed. The performance evaluations corroborate the efficacy of the proposed framework.