Interconnection performance analysis of single phase neural network based NPC and CHB multilevel inverters for grid-connected PV systems

Keddar, M., Doumbia, M. L., Krachai, M. D., Belmokhtar, K. et Midoun, A. H. (2019). Interconnection performance analysis of single phase neural network based NPC and CHB multilevel inverters for grid-connected PV systems. International Journal of Renewable Energy Research, 9 (3). pp. 1451-1461. ISSN 1309-0127 DOI 10.20508/ijrer.v9i3.9593.g7730

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Résumé

This paper presents the interconnection performance analysis of single-phase cascaded H-bridge and neutral point clamped multilevel inverters for grid-connected photovoltaic (PV) applications. Interconnection performance analysis of both inverters was investigated using fixed and variable bands hysteresis current control strategies to ensure a lower current's total harmonic distortion (THD). An intelligent control method based on neural networks was introduced to extract maximum power from the PV modules and to achieve optimal operation of the whole system when connected to the utility grid. Control algorithm was implemented in a microcontroller with interrupt routines priority. Both simulation and experimental results are presented to verify the performance of the proposed control methods. In addition, islanding detection capability of the two topologies was investigated. © 2019 International Journal of Renewable Energy Research.

Type de document:	Article
Mots-clés libres:	Hysteresis control MPPT Multilevel inverter Neural networks Photovoltaic system Electric inverters Electric power system control Electric power system interconnection Hysteresis Interconnection networks (circuit switching) Maximum power point trackers Solar panels Control methods Grid-connected photovoltaic system Multi Level Inverter (MLI) Multilevels Network-based Neural-networks Performances analysis Photovoltaic systems Single phasis Bridge circuits
Date de dépôt:	22 déc. 2022 19:05
Dernière modification:	22 déc. 2022 19:05
Version du document déposé:	Version officielle de l'éditeur
URI:	https://depot-e.uqtr.ca/id/eprint/10328

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