No abstract

No abstract

No abstract

Multilevel inverters have the capability to produce an AC staircase output waveform without using a bulky passive filter. Therefore, the multilevel inverters are gaining more and more popularity, among the different types of inverters, for photovoltaic applications in the modern era of technology. However, if the switching angle arrangement technique is not selected appropriately then the total harmonic distortion of the voltage output waveform may become undesirable. In this paper, Half-Equal-Phase, Feed-Forward and Selective Harmonics Elimination Pulse Width Modulation switching angle arrangement techniques at different power factors (i.e., 1.0, 0.75 and 0.50) are applied to a cascaded battery-boost inverter. PSIM software is used to evaluate and compare the performance of a 9-level cascaded battery-boost inverter with three switching angle arrangement techniques at power factors of 1.0, 0.75, and 0.50, respectively. Simulation results show that the Selective Harmonics Elimination Pulse Width Modulation technique can produce an output voltage and current waveform with the lowest total harmonic distortion. On the other hand, the output current waveform produced by power factor 0.50 had the lowest total harmonic distortion.

Microgrid (MG) is a small-scale grid that consists of multiple distributed energy resources and load demand. The microgrid energy management system (M-EMS) is the decision-making centre of the MG. An M-EMS is composed of four modules which are known as forecasting, scheduling, data acquisition, and human-machine interface. However, the forecasting and scheduling modules are considered the major modules from among the four of them. Therefore, this paper proposed an advanced microgrid energy management system (M-EMS) for grid-connected residential microgrid (MG) based on an ensemble forecasting strategy and grey wolf optimization (GWO) based scheduling strategy. In the forecasting module of M-EMS, the ensemble forecasting strategy is proposed to perform the short-term forecasting of PV power and load demand. The GWO based scheduling strategy has been proposed in scheduling module of M-EMS to minimize the operating cost of grid-connected residential MG. A small-scale experiment is conducted using Raspberry Pi 3 B+ via the python programming language to validate the effectiveness of the proposed M-EMS and real-time historical data of PV power, load demand, and weather is adopted as inputs. The performance of the proposed forecasting strategy is compared with ensemble forecasting strategy-1, particle swarm optimization based artificial neural network, and back-propagation neural network. The experimental results highlight that the proposed forecasting strategy outperforms the other strategies and achieved the lowest average value of normalized root mean square error of day-ahead prediction of PV power and load demand for the chosen day. Similarly, the performance of GWO based scheduling strategy of M-EMS is analyzed and compared for three different scenarios. Finally, the experimental results prove the outstanding performance of the proposed scheduling strategy.