Pyranometers and Reference Cells: Part 2: What Makes the Most Sense for PV Power Plants?; Preprint

Meydbray, Jenya; Riley, Evan; Dunn, Lawrence; Emery, K.; Kurtz, Sarah

doi:10.2172/1059158

Cited by 5 publications

(3 citation statements)

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“…For cloudy days, the SMM was obtained as a fit function through empirical measurements. The ratio (E SiS /E Pyr ) vs. the air mass is plotted in Figure 11 (right), and a polynomial fitting to the cloudy irradiance tendency was obtained and used to correct the GHI measured in cloudy days according to Equation (10).…”

Section: Spectral Mismatch Correction (C S )mentioning

confidence: 99%

“…For cloudy days, the SMM was obtained as a fit function through empirical measurements. The ratio ( / ) vs. the air mass is plotted in Figure 11 (right), and a polynomial fitting to the cloudy irradiance tendency was obtained and used to correct the GHI measured in cloudy days according to Equation (10). It is essential to refer here that the SMM may be strongly influenced by the atmospheric conditions and the PV semiconductor materials [39].…”

Section: Spectral Mismatch Correction (C S )mentioning

confidence: 99%

“…They found the uncertainties in irradiance measurements to be in the order of ±2.4% for PV reference devices and ±5% for thermopile pyranometers. Meydbray et al [10] considered the spectral effect and calculated the deviation of the two sensors in four different locations in the USA. They found that the daily solar irradiation deviation can amount up to 3% and that this deviation is highly variable over days, weeks, and months.…”

Section: Introductionmentioning

confidence: 99%

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Improving the Irradiance Data Measured by Silicon-Based Sensors

2021

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Silicon-based sensors are widely used for monitoring solar irradiance, in particular, in the field of Photovoltaic (PV) applications. We present a method to correct the global horizontal irradiance measured by silicon-based sensors that reduces the difference to the standard thermopile sensor measurements. A major motivation to use silicon-based sensors for the measurements of irradiance is their lower cost. In addition, their response time is much lower, and their spectral response is much closer to that of the PV systems. The analysis of the differences is based on evaluating four parameters that influence the sensor measurements, namely the temperature, cosine error, spectral mismatch, and calibration factor. Based on the analysis, a correction model is applied to the silicon sensors measurements. The model separates measurements under a clear sky and cloudy sky by combining the clearness index and the solar zenith angle. By applying the correction model on the measurements of the silicon-based sensor, the differences between sensor readings have been reduced significantly. The relative root mean squared difference (rRMSD) between the daily solar irradiation measured by both sensors decreased from 10.6% to 5.4% after applying the correction model, while relative mean absolute difference (rMAD) decreased from 7.4% to 2.5%. The difference in total annual irradiation decreased from 70 KWh/m2 (6.5%) to 15 kWh/m2 (1.5%) by the correction. The presented correction method shows promising results for a further improvement in the accuracy of silicon-based sensors.

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Section: Spectral Mismatch Correction (C S )mentioning

confidence: 99%

Section: Spectral Mismatch Correction (C S )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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