The recent advancement in the application of the internet of things in the smart grid has led to an industrial revolution in the power industry. The Industry 4.0 revolution has already set in, allowing computers to interact for an efficient and intelligent approach in solving smart grid issues. multilevel inverters (MLIs) are an integral part of the smart grid system for integrating the distributed generation sources and storage energy systems into the smart grid. It attracted attention in industrial applications as they can handle high power and high voltage with an inherent feature of superior output voltage waveform quality. Moreover, its variant, the switched-capacitor MLI (SCMLI), has the added benefit of lesser DC supply requirement. In this paper, a switched-capacitor multilevel inverter topology has been proposed, which can operate in symmetric and asymmetric mode. The proposed SCMLI generate thirteen and thirty-one level output voltages for symmetric and asymmetric selection of DC voltage sources, respectively. The proposed SCMLI has a smaller number of switching devices for a given output voltage level as compared to other recently proposed topologies. A thorough comparison is presented with the recently proposed topologies on several parameters, including cost function. To validate the proposed topology, symmetric and asymmetric cases were simulated using Matlab^® 2018a and the results were verified using an experimental hardware setup.

A new triple voltage boosting switched-capacitor multilevel inverter (SCMLI) is presented in this paper. It can produce 13-level output voltage waveform by utilizing 12 switches, three diodes, three capacitors, and one DC source. The capacitor voltages are self-balanced as all the three capacitors present in the circuit are connected across the DC source to charge it to the desired voltage level for several instants in one fundamental cycle. A detailed comparative analysis is carried to show the advantages of the proposed topology in terms of the number of switches, number of capacitors, number of sources, total standing voltage (TSV), and boosting of the converter with the recently published 13-level topologies. The nearest level control (NLC)-based algorithm is used for generating switching signals for the IGBTs present in the circuit. The TSV of the proposed converter is 22. Experimental results are obtained for different loading conditions by using a laboratory hardware prototype to validate the simulation results. The efficiency of the proposed inverter is 97.2% for a 200 watt load.

This study assesses the integration of renewable energy sources with grid-connected systems to power electric vehicle charging stations (EVCSs) in Riyadh, Kingdom of Saudi Arabia (KSA), emphasizing its significance for sustainable development. An environmentally friendly EVCS model and a novel metaheuristic optimization technique, the Giza Pyramids Construction Algorithm (GPCA) is employed, to determine the optimal sizing of system components. The objective is to ensure dependability, as determined by the lack of power supply probability (LPSP), while minimizing the levelized cost of energy (LCOE) and total net present cost (TNPC). We assess the efficacy of other algorithms in scaling system components by contrasting the performance of the GPCA with those of the Artificial Hummingbird Algorithm (AHA), Flower Pollination Algorithm (FPA), and Whale Optimization Algorithm (WOA). The study also examines the impact of climatic, financial, and technical factors on system costs, providing insights into the challenges, opportunities, and socio-economic factors influencing the deployment of EV infrastructure in KSA. Findings suggest that the optimal EVCS configuration is a solar photovoltaic/wind turbine (SPV/WT) grid-tied system, resulting in an LCOE of $0.0796/kWh, a TNPC of $102,042, and an annual CO ₂ emission reduction of 18,840 kg. The system achieves a 67.9% renewable penetration rate with 30.8% annual grid electricity purchase and 32.4% sale. Sensitivity analysis on renewable parameters is also conducted, demand charges, grid purchase prices, discount rates, and inflation rates to assess their impact on the system's economic variables. By identifying current constraints and potential strategies, our study aims to inform policy-making and investment decisions for expanding electric vehicle charging infrastructure, with broader implications for developing nations.

Various 7-level switched-capacitor (SC) based converters are reported in the literature which furnishes a voltage gain of 1.5. However, in these topologies, the voltage rating of switches is high and the switch count is more. Further, the charging and discharging action of the switched capacitors, lead to the production of spikes having a large magnitude in source current and charging currents supplied to switched capacitors. Due to this limitation, the SC-based Multilevel Inverter (SC-MLI) cannot be used for high-power applications. To resolve these issues, an SC-MLI in an Active-Neutral-Point-Clamped (ANPC) configuration is proposed in this paper which requires switches having reduced value of voltage stresses and the switch count of the proposed converter is less. Further, to reduce the magnitude of spikes in the source and charging current supplied to switched capacitors, a soft switching circuit is included in the proposed converter. The soft charging circuit includes an inductor and a diode in the charging path of the proposed converter which ensures soft charging of the switched capacitors. The proposed converter can significantly suppress the spikes appearing in the source current by including a small value of inductance connected to the soft charging circuit. The proposed topology requires a single dc source, 2 switched capacitors, and 8 switches to generate 7-level and does not require an auxiliary diode. The absence of an auxiliary diode leads to a further reduction in losses. If the value of the modulation index is reduced to a low value, the proposed topology can maintain the self-balancing of the capacitor voltage. To highlight the various key features of the proposed 7-level topology, its comparison with the recent converter topologies suggested in the literature is carried out. To validate the efficacy of the proposed 7-level SC-MLI, experimental results are included which are captured using a laboratory prototype for different values of soft charging inductor.

A Switched-Capacitor Multilevel Inverter (SCMLI) topology is proposed here, which can generate up to fifteen levels with one unit and can be extended further for getting higher levels. The proposed SCMLI has a lesser number of switching devices with respect to other recently proposed structures presented in this paper. It also has the capacitor self-balancing property. Power loss analysis has also been done using PLECS software. Maximum efficiency of 96.33 % has been achieved. A generalized comparative study has also been carried out with the newly presented topologies in different research articles considering several parameters. In order to validate the structure presented in this paper, simulation is done in Matlab ^® 2018a, and the simulation results obtained are verified using an experimental prototype.

As the applications of power electronic converters increase across multiple domains, so do the associated challenges. With multilevel inverters (MLIs) being one of the key technologies used in renewable systems and electrification, their reliability and fault ride-through capabilities are highly desirable. While using a large number of semiconductor components that are the leading cause of failures in power electronics systems, fault tolerance against switch open-circuit faults is necessary, especially in remote applications with substantial maintenance penalties or safety-critical operation. In this paper, a fault-tolerant asymmetric reduced device count multilevel inverter topology producing an 11-level output under healthy conditions and capable of operating after open-circuit fault in any switch is presented. Nearest-level control (NLC) based Pulse width modulation is implemented and is updated post-fault to continue operation at an acceptable power quality. Reliability analysis of the structure is carried out to assess the benefits of fault tolerance. The topology is compared with various fault-tolerant topologies discussed in the recent literature. Moreover, an artificial intelligence (AI)-based fault detection method is proposed as a machine learning classification problem using decision trees. The fault detection method is successful in detecting fault location with low computational requirements and desirable accuracy.