研究目的
To assess the long-term performance and degradation rates of commercial photovoltaic modules under field conditions over five years.
研究成果
The study demonstrated distinctive degradation behaviors among the five commercial PV module types tested under field conditions. The clear-sky methodology was effective in extracting reliable degradation rates, highlighting the importance of using modeled irradiance data in addition to recorded data. The results contribute to the understanding of PV module performance and degradation under real-world conditions, supporting the need for standardized tools for performance assessment.
研究不足
The study is limited by the reliability of the pyranometer irradiance data, which was affected by sensor malfunction. The clear-sky methodology was used to circumvent this issue, but the results may still be influenced by the specific local conditions of the test facility.
1:Experimental Design and Method Selection:
Five types of commercial PV modules were tested under field conditions for five years. The degradation rates were computed from photovoltaic power normalized by both recorded and modeled solar irradiance.
2:Sample Selection and Data Sources:
The modules included micromorph thin film silicon (lm-Si), cadmium telluride (CdTe), copper indium gallium selenium (CIGS), polycrystalline silicon (poly-Si), and amorphous silicon (a-Si). Data were recorded at the Max Planck Institute for Chemical Energy Conversion in Germany.
3:List of Experimental Equipment and Materials:
PV modules, SMA Sunnyboy 2000HF-30 DC/AC converters, LogMessage data logger, pyranometer from Kipp&Zonen, temperature sensors, Vaisala Weather Transmitter WXT
4:Experimental Procedures and Operational Workflow:
5 Data were recorded once a second, with power, temperature, and irradiance data aggregated and analyzed using Python libraries including pvlib and rdtools.
5:Data Analysis Methods:
Performance ratios were calculated and normalized by both recorded and modeled solar irradiance. Degradation rates were extracted using aggregation and regression strategies.
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