研究目的
To develop a reliable model to predict PV cell and module temperatures (Tc and Tpv) under transient and steady state conditions, taking into account environmental factors such as ambient temperature, wind speed, and solar irradiance.
研究成果
The developed model accurately predicts PV cell and module temperatures under both transient and steady state conditions, with deviations from measured values less than 4%. The model outperforms six other known models in terms of prediction accuracy and is applicable to any geographical site and environmental conditions. Accurate temperature prediction is crucial for monitoring and optimizing PV performance.
研究不足
The model's accuracy can be affected by variations in environmental conditions not fully accounted for, such as wind direction and module inclination. The study primarily focuses on c-Si PV modules, and adaptation to other types may require additional data on their thermal properties.
1：Experimental Design and Method Selection:
The study involved both laboratory and field experiments to measure and predict PV cell and module temperatures under controlled and varying environmental conditions. Theoretical models and algorithms were employed to predict temperature profiles.
2：Sample Selection and Data Sources:
Experiments were conducted on a bare c-Si PV cell by SOLARTEC under laboratory conditions and various c-Si and pc-Si modules (SM55, Bioenergy 195W, Energy Solutions 125W) in field conditions.
3：List of Experimental Equipment and Materials:
Solar light simulator (Solar Light 16S-300-002 Class A Air Mass 1.5 emission spectrum), thin diameter Cu-Const thermocouples, Pt 100 for ambient temperature measurement, and a series of c-Si bare cells and several c-Si and pc-Si modules.
4：5 emission spectrum), thin diameter Cu-Const thermocouples, Pt 100 for ambient temperature measurement, and a series of c-Si bare cells and several c-Si and pc-Si modules.
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: Data were collected and recorded every 1 s using a Data Acquisition System. Temperature profiles were measured and compared with predicted values from the model.
5：Data Analysis Methods:
The time constants of the temperature profiles were determined by fitting exponential functions to the measured data. Statistical analysis was performed to compare predicted and measured values.
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