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Moth flame algorithm-based optimization of a reduced switch multilevel inverter topology suitable for standalone application.

Authors :
Shanono, Ibrahim Haruna
Abdullah, Nor Rul Hasma
Daniyal, Hamdan
Muhammad, Aisha
Source :
Neural Computing & Applications. Jun2024, Vol. 36 Issue 16, p9437-9479. 43p.
Publication Year :
2024

Abstract

The escalating demand for sustainable and environmentally friendly energy sources has driven substantial growth in renewable energy adoption across residential and industrial sectors. To effectively meet this demand, the development of efficient and sustainable renewable energy conversion methods is crucial, with inverters playing a pivotal role in achieving this objective. While researchers strive to enhance inverter power handling capabilities and reduce output harmonic contents, the incorporation of additional power electronic switches and peripheral devices presents challenges such as increased circuit cost, complexity, and size. Additionally, the utilization of high-frequency switching techniques for achieving low output harmonics results in elevated switching losses and electromagnetic interference, adversely affecting sensitive electronic devices. This research introduces a novel approach to address these issues through the introduction of a reduced switch multilevel inverter topology. Unlike existing systems, the proposed topology employs a reduced number of power electronic switches and direct current sources to generate a stable output voltage waveform. Operating in a symmetric mode, the topology achieves a nine-level output voltage with enhanced harmonic elimination capabilities. A multiple-stepped selective harmonic elimination (SHE-PWM) switching control technique, employing a 1/3/3/1 distribution ratio, is utilized to extend the harmonic elimination range from 3 to 7 lower-order harmonics. To optimize the switching angles required for the proposed topology, the moth flame optimization (MFO) algorithm is employed and compared with particle swarm optimization (PSO) and whale optimization algorithms (WOA). The MFO algorithm exhibits faster convergence to the global optima, achieving an optimal fitness value of 3.322e−08 at 0.78 modulation points. This results in total harmonic distortion values of 0.7%, 0.757%, and 1.069% for MFO, PSO, and WOA, respectively, with corresponding total losses of 71.609W, 71.794W, and 79.792W. The proposed inverter topology is simulated using PSIM software and experimentally verified using a typhoon HIL-402 hardware-in-the-loop testing device. The simulation and experimental results provide compelling evidence for the superior performance of the MFO algorithm compared to PSO and WOA in achieving improved inverter performance. The proposed topology, in conjunction with the MFO algorithm, presents a promising solution for efficient and sustainable renewable energy conversion, thereby contributing to the advancement of renewable energy technologies in both residential and industrial settings. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09410643
Volume :
36
Issue :
16
Database :
Academic Search Index
Journal :
Neural Computing & Applications
Publication Type :
Academic Journal
Accession number :
178047824
Full Text :
https://doi.org/10.1007/s00521-024-09576-3