Characterization of Photovoltaic Modules under Uniform Snow Covers

• Published in: Conference 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
• Language: English
• 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
• ISSN: 2576-702X
• DOI: 10.1109/IAS62731.2025.11061649

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.