Characterization of Photovoltaic Modules under Uniform Snow Covers

The performance of Photovoltaic modules (PV) is affected in cold climate regions with significant snowfall during the winter season. This paper identifies the electrical characterizations of snow-covered PV modules. A self-adaptive Differential Evolution (DE) optimization technique is used to determ...

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Bibliographic Details
Published inConference record of the Industry Applications Conference pp. 1 - 5
Main Authors Rezzouk, Hanane, Djabali, Sarah, Cretu, Ana-Maria, Taheri, Shamsodin, Ehsan, Jamshidpour, Miloud, Rezkallah
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.06.2025
Subjects
Accuracy
Beer-Lambert Law
Data models
Meteorology
Parameters Extraction
Photovoltaic Module
Prediction algorithms
Predictive models
Self-Adaptive Differential Evolution
Single Diode Model
Snow
Snow-Accretion
Software algorithms
Software tools
Solar panels
Solar radiation
Online AccessGet full text
ISSN2576-702X
DOI10.1109/IAS62731.2025.11061649

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Summary:The performance of Photovoltaic modules (PV) is affected in cold climate regions with significant snowfall during the winter season. This paper identifies the electrical characterizations of snow-covered PV modules. A self-adaptive Differential Evolution (DE) optimization technique is used to determine the Single-Diode Model (SDM) parameters of the module and Beer-Lambert law is used to model the solar penetration in the snowpack. Measurements obtained from outdoor tests on different PV technologies (a mono-crystalline ET-M53695 system, a thin film FS-275 PV system and a CS6P-260P polycrystalline system) are used to examine the accuracy of the extracted parameters in predicting the electrical behavior of snow-covered PV modules. In addition, the impact of the snow depth on the power losses of PV modules is determined.
ISSN:2576-702X
DOI:10.1109/IAS62731.2025.11061649