Artificial neural network assisted variable step size incremental conductance MPPT method with adaptive scaling factor

Song Pei Ye, Yi Hua Liu*, Chun Yu Liu, Kun Che Ho, Yi Feng Luo

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

5 Scopus citations

Abstract

In conventional adaptive variable step size (VSS) maximum power point tracking (MPPT) algorithms, a scaling factor is utilized to determine the required perturbation step. However, the performance of the adaptive VSS MPPT algorithm is essentially decided by the choice of scaling factor. In this paper, a neural network assisted variable step size (VSS) incremental conductance (IncCond) MPPT method is proposed. The proposed method utilizes a neural network to obtain an optimal scaling factor that should be used in current irradiance level for the VSS IncCond MPPT method. Only two operating points on the characteristic curve are needed to acquire the optimal scaling factor. Hence, expensive irradiance and temperature sensors are not required. By adopting a proper scaling factor, the performance of the conventional VSS IncCond method can be improved, especially under rapid varying irradiance conditions. To validate the studied algorithm, a 400 W prototyping circuit is built and experiments are carried out accordingly. Comparing with perturb and observe (P&O), α-P&O, golden section and conventional VSS IncCond MPPT methods, the proposed method can improve the tracking loss by 95.58%, 42.51%, 93.66%, and 66.14% under EN50530 testing condition, respectively.

Original languageEnglish
Article number43
JournalElectronics (Switzerland)
Volume11
Issue number1
DOIs
StatePublished - 01 01 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Incremental conductance method
  • Maximum power point tracking
  • Neural network
  • Variable step size

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