Multipurpose Power Converter for Non-Grid-Connected Microsystems

Vovos, Panagis N.; Georgakas, Konstantinos G.

doi:10.1515/ijeeps-2014-0129

Cited by 2 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To overcome these limitations, work in [18], [19] propose use of quadratic buck-boost converter, and high-gain buck-boost converter for better total harmonic distortion (THD) response, which makes it useful for real-time operations. Similar models are discussed in [20], [21], that aim at power converters, integrated high-quality rectifier regulator, and genetic algorithm (GA) with MLI and in [22]- [25] which aims at reducing harmonics from outputs, thereby improving conversion efficiency levels. Extensions to these models are discussed in [26]- [31] which propose use of switched reluctance motor (SRM), neutral point clamped (NPC), dynamic voltage restorer (DVR) topologies, high brightness light emitting diode (HB-LED), and non-isolated buck-boost dc/dc converters.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of an efficient convolutional buck-boost converter for hybrid bioinspired parameter tuning

Mutta¹,

Goswami²

2023

Bulletin EEI

View full text Add to dashboard Cite

Power-electronic systems with voltage boosts use buck-boost converters. These converters suppress current and invert voltage to improve voltage swing. Power-electronic systems with voltage boosts use buck-boost converters that suppress current and invert voltage to improve voltage swings. Researchers propose many converter models, but their total harmonic distortion (THD) limits their scalability. Harmonics from additional current components increase THD. The model filters excessive currents using inductor-based storage, capacitive filters, and resistive circuits. However, these models are unstable, reducing their performance in large converter circuits. This text proposes a novel convolutional neural network (CNN) with a hybrid bioinspired model based on genetic algorithm (GA) and particle swarm optimization (PSO) to overcome this limitation. Estimating internal buck and boost parameters efficiently reduces reverse currents. These parameters include inductor current ripple, recommended inductance, internal switch current limit, and switching frequency. The model finds low-power, high-efficiency buck-boost configurations based on these values. Incremental learning operations tuned the GA model, which was applied to many buck-boost configurations. The proposed model had a 5.9% lower delay, 16.2% lower harmonics, and 4.6% better power efficiency than state-of-the-art buck-boost models.

show abstract