A novel multilevel inverter topology with reduced number of component count and total standing voltage for renewable energy conversion system

Yarlagadda, Anil Kumar; Verma, Vimlesh

doi:10.1016/j.ref.2022.09.004

Cited by 6 publications

(1 citation statement)

References 34 publications

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“…Consequently, it exhibits reduced switching losses, resulting in enhanced energy efficiency. Furthermore, the output waveforms of the MLI exhibit a stepped configuration, leading to diminished harmonic content and lower rates of voltage change when compared to square wave or PWM inverters 33 . Consequently, the MLI proved to be well-suited for applications that prioritize low harmonic distortion and minimize voltage stress on connected devices 34 .…”

Section: Complete System Modelingmentioning

confidence: 99%

A grey wolf optimization-based modified SPWM control scheme for a three-phase half bridge cascaded multilevel inverter

Nasser,

Refky,

Shatla

et al. 2024

Sci Rep

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The Multilevel inverter (MLI) plays a pivotal role in Renewable Energy (RE) systems by offering a cost-effective and highly efficient solution for converting DC from Photovoltaic (PV) sources into AC at high voltages. In addition, an innovative technology holds immense significance as it not only enables the seamless integration of PV systems into the grid but also ensures optimal power generation, thereby contributing to the widespread adoption of RE and fostering a sustainable future. This paper presents a modified sinusoidal pulse width modulation (SPWM) control scheme for a three-phase half-bridge cascaded MLI-powered PV sources. The selection of the MLI configuration is motivated by its reduced number of switching components, which enhances system reliability and simplifies experimental implementation. Compared to the SPWM schemes which require (m−1) carriers that make the generation of the pulse circuit very complex, the proposed control scheme requires only three signals: a carrier signal, a triangular waveform, and a modulating signal. This approach significantly reduces the complexity of control and facilitates practical implementation. The proposed control scheme simulation is verified using MATLAB/SIMULINK Software. The grey wolf optimization (GWO) algorithm is implemented to determine the optimal switching angles of the proposed control scheme. The Total Harmonic Distortion (THD) objective is selected to be the fitness function to be minimized for improving the quality of the output waveforms. For verification, the results of the proposed GWO-based modified SPWM control scheme are compared with those obtained using both the Particle swarm Optimization (PSO) and Genetic algorithm (GA) used in the literature. Simulation results declared that the proposed control scheme improves performance, especially THD which is minimized to 6.8%. Experimental validation has been conducted by building a laboratory prototype of the proposed system. The experimental and simulation results gave acceptable and limited convergent results considering the experimental difficulties.

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Section: Complete System Modelingmentioning

confidence: 99%

A grey wolf optimization-based modified SPWM control scheme for a three-phase half bridge cascaded multilevel inverter

Nasser,

Refky,

Shatla

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

13 Level Switched‐Capacitor Multilevel Converter with High Gain for Grid Connected Solar Photovoltaic Applications

Iqbal,

Tayyab,

Rehman

et al. 2024

Multilevel Converters

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A novel multilevel inverter topology with reduced number of component count and total standing voltage for renewable energy conversion system

Cited by 6 publications

References 34 publications

A grey wolf optimization-based modified SPWM control scheme for a three-phase half bridge cascaded multilevel inverter

A grey wolf optimization-based modified SPWM control scheme for a three-phase half bridge cascaded multilevel inverter

13 Level Switched‐Capacitor Multilevel Converter with High Gain for Grid Connected Solar Photovoltaic Applications

An extensive critique on machine learning techniques for fault tolerance and power quality improvement in multilevel inverters

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