Comparative Study Of P&O and PSO Particle Swarm Optimization MPPT Controllers Under Partial Shading-Reference-Cited by-同舟云学术

Comparative Study Of P&O and PSO Particle Swarm Optimization MPPT Controllers Under Partial Shading

Published:2022-10-06 Issue: Volume:4 Page:45-50
ISSN:2769-2507
Container-title:International Journal of Electrical Engineering and Computer Science
language:
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Author:

Brahmi Mahbouba¹,Regaya Chiheb Ben¹,Hamdi Hichem¹,Zaafouri Abderrahmen¹

Affiliation:

1. Research Laboratory LISIER, National High School of Engineers of Tunis ENSIT, University of Tunis, 5 Av. Taha Hussein, Tunis, 1008, TUNISIA

Abstract

Maximum power point tracking (MPPT) is a vital and essential requirement for photovoltaic (PV) systems under normal irradiance and partial shading conditions (PSCs). Although, perturb and observe (P&O) algorithm does not map the global maximum power point (GMPP), whereas the algorithm for particle swarm optimization (PSO) tracks it efficiently. This paper explores the rapid determination of GMPP under PSCs using a proposed particle swarm optimization MPPT technique which operates in conjunction with a boost converter. To achieve this analysis, MATLAB/SIMIULINK is used. The results of the simulation illustrate the high tracking performance of the proposed technique under various irradiance patterns.

Publisher

World Scientific and Engineering Academy and Society (WSEAS)

Reference8 articles.

1. M. Brahmi, B. R. Chiheb, H. Hamdi and Z. Abderrahmen (2022), “Comparative Study of P&O and PSO Particle Swarm Optimization MPPT Controllers for Photovoltaic Systems”, 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT).

2. H. Hamdi, B. R. Chiheb and Z. Abderrahmen (2019), “Performance improvement of a photovoltaic system with a radial basis function network based on particle swarms optimization”, 2019 International Conference on Signal, Control and Communication (SCC).

3. Hamdi, H., Regaya, C. B., & Abderrahmen, Z. (December 2019). A sliding-neural network control of induction-motor-pump supplied by photovoltaic generator.

4. Hichem, H., Chiheb, B. R., & Abderrahmen, Z. (2019). Performance improvement of a photovoltaic system with a radial basis function network based on particle swarms optimization.

5. Hichem, H., Chiheb, B. R., & Abderrahmen, Z. (May 2019). Real-time study of a photovoltaic system with boost converter using the PSO-RBF neural network algorithms in a MyRio controller (Vol. 183).

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