研究目的
Investigating the effects of snow on photovoltaic (PV) module performance and proposing a novel methodology for PV modelling under snowy conditions.
研究成果
The proposed PSO-based PV model accurately predicts the behaviour of snow-covered PV modules by coupling the albedo and extinction of solar radiation based on the Giddings and LaChapelle theory. The model was validated experimentally and using real data from a PV farm, showing good agreement with experimental results. It offers a significant advantage in predicting electric characteristics of PV modules under different snow conditions, contributing to improved PV system performance in cold climates.
研究不足
The study focuses on uniformly snow-covered PV modules and does not address non-uniform snow coverage. The model's accuracy under varying snow conditions and its integration into maximum power point tracking (MPPT) boost converters are areas for future research.
1:Experimental Design and Method Selection:
The study employs the single-diode-five-parameter equivalent circuit model for PV modules under snowy conditions, using the Giddings and LaChapelle theory to estimate solar radiation penetration through snow. The particle swarm optimisation (PSO) algorithm is used to optimise model parameters.
2:Sample Selection and Data Sources:
Three different types of PV module technologies were tested outdoors under various cold weather conditions. Real data from a 12-MW grid-connected PV farm's SCADA system were also used.
3:List of Experimental Equipment and Materials:
PV modules (ET-M53695 monocrystalline, CS6P-260P polycrystalline, FS-275 thin film), HT Instruments I-V 400 PV Panel Analyser, irradiance meter test kit, Fluke 62 Mini infrared thermometer, digital scale, magnifying glass, millimetre-scale grid.
4:Experimental Procedures and Operational Workflow:
Measurements of electric characteristics of PV modules covered with snow were conducted. Snow properties and depth, irradiance, and temperature were recorded. The PSO algorithm was applied to update PV module parameters based on environmental conditions.
5:Data Analysis Methods:
The root mean square error (RMSE) between measured and calculated pairs of voltage and current values was used to evaluate the model's accuracy.
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