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Shading fault detection in a grid-connected PV system using vertices principal component analysis

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  • Rouani, Lahcene
  • Harkat, Mohamed Faouzi
  • Kouadri, Abdelmalek
  • Mekhilef, Saad

Abstract

Partial shading severely impacts the performance of the photovoltaic (PV) system by causing power losses and creating hotspots across the shaded cells or modules. Proper detection of shading faults serves not only in harvesting the desired power from the PV system, which helps to make solar power a reliable renewable source, but also helps promote solar versus other fossil fuel electricity-generation options that prevent making climate change targets (e.g. 2015’s Paris Agreement) achievable. This work focuses primarily on detecting partial shading faults using the vertices principal component analysis (VPCA), a data-driven method that combines the simplicity of its linear model and the ability to consider the uncertainties of the different measurements of a PV system in an interval format. Data from a grid-connected monocrystalline PV array, installed on the rooftop of the Power Electronics and Renewable Energy Research Laboratory (PEARL), University of Malaya, Malaysia, have been used to train the VPCA model. To prove the effectiveness of this VPCA method, four partial shading patterns have been created. The obtained performance has, then, been tested against a regular PCA. In addition to its ability to acknowledge the uncertainty of a PV system, the VPCA method has shown an enhanced performance of detecting partial shading fault in comparison with the standard PCA. Also, included in the article is an extension of the contribution plot diagnosis-based method, of the Q-statistic, to the interval-valued case aiming to pinpoint the out-of-control variables.

Suggested Citation

  • Rouani, Lahcene & Harkat, Mohamed Faouzi & Kouadri, Abdelmalek & Mekhilef, Saad, 2021. "Shading fault detection in a grid-connected PV system using vertices principal component analysis," Renewable Energy, Elsevier, vol. 164(C), pages 1527-1539.
  • Handle: RePEc:eee:renene:v:164:y:2021:i:c:p:1527-1539
    DOI: 10.1016/j.renene.2020.10.059
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    5. Pillai, Dhanup S. & Shabunko, Veronika & Krishna, Amal, 2022. "A comprehensive review on building integrated photovoltaic systems: Emphasis to technological advancements, outdoor testing, and predictive maintenance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).

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