Research Article Open Access

Efficiency of Hybrid MPPT Techniques Based on ANN and PSO for Photovoltaic Systems under Partially Shading Conditions

Max Tatsuhiko Mitsuya1 and Anderson Alvarenga de Moura Meneses1
  • 1 Federal University of Western Pará, Brazil
American Journal of Engineering and Applied Sciences
Volume 12 No. 4, 2019, 460-471

DOI: https://doi.org/10.3844/ajeassp.2019.460.471

Submitted On: 28 August 2019 Published On: 3 October 2019

How to Cite: Mitsuya, M. T. & de Moura Meneses, A. A. (2019). Efficiency of Hybrid MPPT Techniques Based on ANN and PSO for Photovoltaic Systems under Partially Shading Conditions. American Journal of Engineering and Applied Sciences, 12(4), 460-471. https://doi.org/10.3844/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.

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Keywords

  • Photovoltaic Systems
  • Hybrid Maximum Power Point Tracking
  • Artificial Neural Network
  • Particle Swarm Optimization
  • Perturb and Observe