@article {10.3844/ajeassp.2019.460.471,
article_type = {journal},
title = {Efficiency of Hybrid MPPT Techniques Based on ANN and PSO for Photovoltaic Systems under Partially Shading Conditions},
author = {Mitsuya, Max Tatsuhiko and de Moura Meneses, Anderson Alvarenga},
volume = {12},
number = {4},
year = {2019},
month = {Oct},
pages = {460-471},
doi = {10.3844/ajeassp.2019.460.471},
url = {https://thescipub.com/abstract/ajeassp.2019.460.471},
abstract = {Hybrid Maximum Power Point Tracking (MPPT) algorithms have been investigated as an alternative to improve the performance of conventional MPPT, such as Perturb and Observe (P&O), Incremental Conductance (InC) and Hill Climbing (HC) in Photovoltaic (PV) systems under Partially Shading Co+ndition (PSC). In the present article, Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) hybridized with P&O algorithm for MPPT are compared in terms of power efficiency. This paper not only compare hybrid MPPT methods against conventional MPPT, but also provide a comparison between two different hybrid techniques. In order to evaluate the performance of such hybrid methods, a PV system was computationally modelled and different PSC scenarios were implemented in MATLAB®/Simulink, as well as the hybrid methods ANN + P&O and PSO + P&O. The hybrid methods ANN + P&O and PSO + P&O successfully improve the efficiency of P&O algorithm, respectively achieving 98.93% and 92.96% on average in the PSC scenarios tested, whereas P&O achieved 88.27% on average in such scenarios.},
journal = {American Journal of Engineering and Applied Sciences},
publisher = {Science Publications}
}