Uncertainty Calculation Method for Photodiode Pyranometers

Abstract

For project development and monitoring of large-scale solar power plants, solar irradiance ground measurements are of crucial importance. Photodiode pyranometers are widely used for such applications, especially at remote locations. The advantages compared to most thermopile pyranometers are a fast response time in the order of microseconds, lower costs, no thermal offsets, and a higher robustness against sensor soiling. In contrast to most thermopile sensors, which use clear domes as entrance windows, most photodiode pyranometers use diffusor disks as entrance windows. The scattering contribution of the soiling losses is smaller in the case of a diffusor and therefore the soiling effect is reduced for most photodiode pyranometers. [1,2] Note that ventilators might also reduce the soiling losses. However, photodiode pyranometers have higher measurement errors compared to thermopile pyranometers which are partly systematic and can cause a noticeable bias in solar resource measurements. [3] In comparison with the typical domes of thermopile pyranometers, the diffusor disk of photodiode pyranometers causes a comparably high incidence angle dependence, although its shape is already designed to limit the effect. [4] A fully flat diffusor disk, only exposed to the radiation via its up-facing surface, would cause lower sensitivities for high incidence angles. This is related to the increase of the surface reflectance with larger incidence angles and the extension of the light path through the diffusor. Most diffusor disks are therefore curved or use not only the upper circular surface of a cylinder-shaped receiver, but also the lateral area. Furthermore, photodiode pyranometers show a higher temperature dependence than modern thermopile pyranometers and less stability of the calibration. [5] The errors of photodiode pyranometers, which are probably most complex to correct, stem from the photodiode's wavelength-dependent spectral response (SR). [6] The responsivity to broadband irradiance therefore depends on the solar spectrum. Deviations between the current spectrum and the spectrum used for calibration will hence cause measurement errors, the so-called spectral error. This error depends on the temperature-dependent SR of the pyranometer, the air mass (AM), and the atmospheric conditions at the site at the time of measurement.The correction factors and calibration of rotating shadowband irradiometers (RSIs) are discussed in Section 2 and the uncertainty of photodiode pyranometer measurements is discussed in Section 3. The calculation method for the uncertainty of GHI, DNI, and DHI is presented in Section 4, first regarding the spectral uncertainty with respect to uncorrected and corrected measurements and then regarding further uncertainty contributions. In Section 5, the validation of the method is presented with the application of the method at the six measurement stations with RSI and reference data in different climates. Finally, conclusions are presented and summarized.

Autonomous measurement system for photovoltaic and radiometer soiling losses

“…In the case of PV systems, it has been estimated that soiling reduces the global power production by 3% to 4% 1 . For radiometers such as pyranometers that are used to measure global horizontal irradiance (GHI) or global tilted irradiance (GTI), several studies investigated the signal reduction 2,3 and have shown that soiling can cause relevant uncertainty contributions 4 5 and to avoid false alarms related to other issues that might cause underperformance 6 .…”

“…1 For radiometers such as pyranometers that are used to measure global horizontal irradiance (GHI) or global tilted irradiance (GTI), several studies investigated the signal reduction 2,3 and have shown that soiling can cause relevant uncertainty contributions. 4 The knowledge of the current soiling losses of a PV installation can be used to optimize the cleaning schedule 5 and to avoid false alarms related to other issues that might cause underperformance. 6 Underperformance can be detected by comparing measured to modeled PV production derived using pyranometer or reference cell measurements.…”