Selective harmonic elimination in PUC‐5 multilevel inverter using hybrid IGWO‐DE algorithm
The use of a multilevel inverter has received significant attention in recent years due to its numerous advantages. Because of the widespread use of multilevel inverters in industries and applications that require a wide range of voltages, achieving high‐quality voltage has presented a number of cha...
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Veröffentlicht in: | Engineering Reports 2024-10, Vol.6 (10), p.n/a |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | The use of a multilevel inverter has received significant attention in recent years due to its numerous advantages. Because of the widespread use of multilevel inverters in industries and applications that require a wide range of voltages, achieving high‐quality voltage has presented a number of challenges. Many studies have been carried out to address the problem of unwanted harmonics in multilevel inverters. The inverter switching can be done at a low frequency using the Selective Harmonic Elimination (SHE) technique, and the unwanted harmonics can be significantly reduced; however, the issue with SHE is solving the transcendental equations and determining the optimum switching angle. To address this problem, a new and improved hybrid algorithm is proposed that combines two evolutionary algorithms, gray wolf optimization (GWO) with an improved and new convergence factor and Differential evolution (DE) with a dynamic scaling factor using a crossover operator. In this paper, the optimum switching angles for a packed U cell 5‐level inverter are estimated using the proposed algorithm for distinct modulation index values, and simulation results are compared with different algorithms. The simulation result of the proposed algorithm, IGWO‐DE is confirmed through experiment.
The inverter switching can be done at a low frequency using the Selective Harmonic Elimination (SHE) technique, and the unwanted harmonics can be significantly reduced; however, the issue with SHE is solving the transcendental equations and determining the optimum switching angle. A new and improved hybrid algorithm is proposed that combines two evolutionary algorithms, gray wolf optimization (GWO) with an improved and new convergence factor and Differential evolution (DE) with a dynamic scaling factor using a crossover operator. |
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ISSN: | 2577-8196 2577-8196 |
DOI: | 10.1002/eng2.12883 |