MPPT perturbation optimization of photovoltaic power systems based on solar irradiance data classification

Ke Yan, Yang Du*, Zixiao Ren

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

59 Citations (Scopus)

Abstract

The tracking accuracy and speed are two main issues for the fixed step perturb-and-observe maximum power point tracking (MPPT) method. This study proposes a novel solution to balance the tradeoff between performance and cost of the MPPT method. The perturbation step size is determined off-line for a specific location based on the local irradiance data. The support vector machine is employed to automatically classify the desert or coastal locations using historical irradiance data. The perturbation step size is optimized for better system performance without increasing the control complexity. Simulations and experiments have been carried out to verify the effectiveness and superiority of the proposed method over existing approaches. The experimental results show a 5.8% energy generation increment by selecting optimal step sizes for different irradiance data types.

Original languageEnglish
Article number8356136
Pages (from-to)514-521
Number of pages8
JournalIEEE Transactions on Sustainable Energy
Volume10
Issue number2
DOIs
Publication statusPublished - Apr 2019

Keywords

  • Maximum power point tracking (MPPT)
  • PV power system
  • classification
  • irradiance
  • machine learning
  • support vector machine (SVM)

Fingerprint

Dive into the research topics of 'MPPT perturbation optimization of photovoltaic power systems based on solar irradiance data classification'. Together they form a unique fingerprint.

Cite this