Analysis of solar panel's lumped equivalent circuit parameters using LASSO

Martin Garaj, Henry Shu Hung Chung, Alan Wai-Lun Lo, Huai Wang

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    2 Scopus citations

    Abstract

    The power output of a solar panel depends on the level of degradation and external factors, such as temperature and solar irradiation. The changes in the power output caused by temperature and irradiation fluctuations coincide with the changes due to degradation. Therefore, a common way of degradation determination requires the knowledge of external factors. This work investigates a modeling of the panel power output at the reference state without the knowledge of the external factors. Firstly, a sparse regression modeling is used to identify relevant lumped equivalent circuit parameters, which capture the reference state of the solar panel. The p-n junction capacitance is found to be an important indicator of external factors tied to the panel's state of health. Secondly, a set of regression models for modelling the solar panel reference state are tested to distinguish a permanent 5% drop in power output from a regular temperature and irradiation fluctuations, without the information of the temperature and irradiation.

    Original languageBritish English
    Title of host publication2019 IEEE Energy Conversion Congress and Exposition, ECCE 2019
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages3427-3432
    Number of pages6
    ISBN (Electronic)9781728103952
    DOIs
    StatePublished - Sep 2019
    Event11th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2019 - Baltimore, United States
    Duration: 29 Sep 20193 Oct 2019

    Publication series

    Name2019 IEEE Energy Conversion Congress and Exposition, ECCE 2019

    Conference

    Conference11th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2019
    Country/TerritoryUnited States
    CityBaltimore
    Period29/09/193/10/19

    Keywords

    • Diagnostics
    • Photovoltaic panel
    • Regression

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