Diagnostic module for series-connected photovoltaic panels

Martin Garaj, Kelvin Yiwen Hong, Henry Shu Hung Chung, Alan Wai lun Lo, Huai Wang

    Research output: Contribution to journalArticlepeer-review

    3 Scopus citations

    Abstract

    An online diagnostic module for condition monitoring of two series-connected photovoltaic panels is presented. The technique is based on firstly perturbing the terminal voltages and currents of the panels with a switched-inductor circuit, which can also be used for differential power processing, to obtain the large-signal dynamic current-voltage characteristics of the panels. An evolutionary algorithm is used to estimate the intrinsic parameters of the panels from the time series of the sampled panel current and voltage. The conditions of the panels are monitored by observing the long-term changes in the extracted intrinsic parameters. Prototype data acquisition module for studying the conditions of solar panels of different technologies (amorphous and crystalline silicon) with different degrees of damage has been built and evaluated. Results reveal that the estimated intrinsic parameters from large-signal dynamic characteristic correlate with the observed health status of the tested panels. Theoretical predictions are favorably compared with experimental measurements.

    Original languageBritish English
    Pages (from-to)243-259
    Number of pages17
    JournalSolar Energy
    Volume196
    DOIs
    StatePublished - 15 Jan 2020

    Keywords

    • Evolutionary computation
    • Fault diagnosis
    • Photovoltaic panels
    • Photovoltaic systems

    Fingerprint

    Dive into the research topics of 'Diagnostic module for series-connected photovoltaic panels'. Together they form a unique fingerprint.

    Cite this