Accurately determining PV module performance in the field requires measurement of solar irradiance reaching the PV panel at a high level of accuracy and known uncertainty. Silicon detectors used in various solar energy measuring instruments including reference cells are potentially an attractive choice for multiple reasons that include faster responsivity than thermopile detectors, cheaper cost and lower maintenance. The main drawback though is the fact that the silicon detectors are only spectrally responsive in a narrow part of the solar spectrum. Therefore, to determine broadband solar irradiance a calibration factor that converts the narrowband response to broadband is required. Normally this calibration factor is a single number determined under standard conditions but then used for various scenarios including varying air-mass, panel orientation and atmospheric conditions. This would not have been an issue if all wavelengths that form the broadband spectrum responded uniformly to atmospheric constituents. Unfortunately the scattering and absorption signature varies widely across wavelengths and the calibration factor computed under certain test conditions is not appropriate for other conditions. This paper lays out the issues that will arise from the use of silicon detectors for PV performance measurement in the field. We also present a comparison of simultaneous spectral and broadband measurements from silicon and thermopile detectors and estimated measurement errors when using silicon devices for both array performance and resource assessment

M Sengupta

P Gotseff

T Stoffel

Sengupta, M.

Gotseff, P.

Stoffel, T.

UNT Digital Library

NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.   Contract No. DE-AC36-08GO28308    Evaluation of Photodiode and Thermopile Pyranometers for Photovoltaic Applications Preprint M. Sengupta, P. Gotseff, and T. Stoffel To be presented at the 27th European Photovoltaic Solar Energy Conference and Exhibition Frankfurt, Germany September 24–28, 2012 Conference Paper NREL/CP-5500-56540 September 2012   NOTICE The submitted manuscript has been offered by an employee of the Alliance for Sustainable Energy, LLC (Alliance), a contractor of the US Government under Contract No. DE-AC36-08GO28308. Accordingly, the US Government and Alliance retain a nonexclusive royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for US Government purposes. This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights.  Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof.  The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof. Available electronically at http://www.osti.gov/bridge Available for a processing fee to U.S. Department of Energy and its contractors, in paper, from: U.S. Department of Energy Office of Scientific and Technical Information P.O. Box 62 Oak Ridge, TN 37831-0062 phone:  865.576.8401 fax: 865.576.5728 email:  mailto:reports@adonis.osti.gov Available for sale to the public, in paper, from: U.S. Department of Commerce National Technical Information Service 5285 Port Royal Road Springfield, VA 22161 phone:  800.553.6847 fax:  703.605.6900 email: orders@ntis.fedworld.gov online ordering:  http://www.ntis.gov/help/ordermethods.aspx Cover Photos: (left to right) PIX 16416, PIX 17423, PIX 16560, PIX 17613, PIX 17436, PIX 17721  Printed on paper containing at least 50% wastepaper, including 10% post consumer waste. 1 EVALUATION OF PHOTODIODE AND THERMOPILE PYRANOMETERS FOR PHOTOVOLTAIC APPLICATIONS Manajit Sengupta, Peter Gotseff, and Thomas Stoffel National Renewable Energy Laboratory 15013 Denver West Parkway, Golden, CO 80401-3305, USA ABSTRACT: Accurately determining photovoltaic (PV) module performance in the field requires measuring solar irradiance reaching the PV panel at a high level of accuracy and known uncertainty. Silicon detectors used in various solar energy measuring instruments that include reference cells are a potentially attractive choice for multiple reasons, including faster responsivity than thermopile detectors, cheaper cost, and lower maintenance. The main drawback, however, is that the silicon detectors are only spectrally responsive in a narrow part of the solar spectrum; therefore, to determine broadband solar irradiance, a calibration factor that converts the narrowband response to broadband is required. Normally, this calibration factor is a single number determined under standard conditions but used for various scenarios, including varying airmass, panel orientation, and atmospheric conditions. This would not have been an issue if all wavelengths that form the broadband spectrum responded uniformly to atmospheric constituents. Unfortunately, the scattering and absorption signature varies widely across wavelengths, and the calibration factor computed under certain test conditions is not appropriate for other conditions. This paper lays out the issues that will arise from the use of silicon detectors for PV performance measurement in the field. We also present a comparison of simultaneous spectral and broadband measurements from silicon and thermopile detectors and estimated measurement errors when using silicon devices for both array performance and resource assessment. Keywords: spectral response, photodiodes, pyranometer, direct normal irradiance, global horizontal irradiance 1 INTRODUCTION Silicon photodiode-based pyranometers have been used to measure global horizontal irradiance (GHI), primarily in agricultural networks, for decades. These radiometers are also popular for applications in solar energy conversion. They are currently used in numerous locations to measure GHI for various purposes, including solar resource assessment and photovoltaic (PV) performance [1]. PV performance testing requires accurate measurements of both power output by PV panels and solar energy incident on the panels (plane-of- array, or POA, irradiance). These silicon devices have become popular mainly because of their low cost, ease of maintenance, and fast time response for high frequency data. Silicon photodiode pyranometers provide limited spectral response, as shown in Fig. 1. The direct normal irradiance (DNI) spectral irradiance shown in Fig. 1 illustrates the magnitude of this limitation. The calibration of pyranometers is based on simultaneous measurements of solar irradiance by a broadband thermopile reference (REF) and the unit under test (UUT) [2]. The resulting pyranometer responsivity is computed as the ratio UUT (µV) / REF (Wm-2). For the photodiode- based pyranometer, this calculation represents the energy collected by a silicon device to the total energy available in the solar spectrum. Calibration data are generally collected throughout the day under clear-sky conditions. Unfortunately, the solar spectrum does not change uniformly with increasing airmass; therefore, the ratio of the energy gathered by a silicon photodiode pyranometer compared with the total energy in the solar spectrum will vary during the day. This paper seeks to understand the impact of this variability and whether the use of a constant calibration coefficient results in significant error in estimation of broadband POA irradiance using a silicon photodiode pyranometer.  Fig. 1. Spectral response function of a silicon photodiode pyranometer (in green) shown along with a spectral DNI measurement from a summer and winter day (shown in red and blue, respectively). 2 MOTIVATION We designed a single-axis tracking device at the National Renewable Energy Laboratory (NREL) and mounted multiple silicon devices, including a LICOR model LI-200, an Apogee model SP 110, an IMT reference cell, as well as a Kipp and Zonen model CMP 11 thermopile pyranometer. The goal of this instrument package was to investigate the possibility of deploying such single-axis tracking devices in the field for solar resource assessment relevant to a similarly tracking PV plant (Fig. 2). We observed that the silicon-based devices had significant measurement difference when compared to each other based on the manufacturer’s calibration. We also observed 2 that although the LI-200 measurements agreed with the CM 11 at solar noon, over-prediction of GHI occurred earlier in the morning and later in the afternoon. (Fig. 2). The CM 11 thermopile device was validated against a reference CM 21 at solar noon.  Fig. 2. Silicon and thermopile device measurement mounted on a single-axis tracker. GHI measurements from a well-calibrated instrument are also shown. The silicon devices were scaled to the LI-200 using the solar noon offset as the correction, as shown in Fig. 3. It was clear that all silicon devices agreed with the CM 11 thermopile instrument at solar noon but over-predicted both in the morning and afternoon.  Fig. 3. Silicon and thermopile device measurement mounted on a single-axis tracker. The measurements from the silicon devices were scaled to the LI-200. GHI measurements from a well-calibrated instrument are also shown. The difference between the silicon and thermopile devices was significant enough that over-prediction similar to that observed in this case will result in significant errors in PV performance evaluation if silicon devices are used. In this paper, we investigate the impact of spectral sensitivity of silicon devices and whether a static calibration of the silicon devices leads to errors in measurement when compared to broadband measurements by well-calibrated thermopile devices. 3 METHODOLOGY AND RESULTS The spectral distributions of measured DNI and GHI changed as the solar zenith angle changes during the course of the day. Fig. 4 shows how the spectral DNI changed during the course of a clear day. Fig. 5 shows how the spectral GHI changed during the same day.  Fig. 4. Hourly DNI spectra for April 6, 2012, measured from 6 a.m. to 12 p.m. The dashed black line represents the spectral response of the LI-200 instrument. The dotted black line represents the spectral response of the diffuser on the LI-200.  Fig. 5. Hourly GHI spectra for April 6, 2012, measured from 6 a.m. to 12 p.m. The dashed black line represents the spectral response of the LI-200 instrument. The dotted black line represents the spectral response of the diffuser on the LI-200. The spectral DNI was measured using a Kipp and Zonen PGS 100 spectrophotometer with the spectra being measured from 350 nanometers (nm) to 1050 nm at a resolution of approximately 4 nm. A LI-COR LI-1800 spectroradiometer measures spectral GHI between the wavelengths of 350 nm and 1100 nm. The DNI spectrum for various times showed that the distribution changed during the day as the airmass changed (see Fig. 4). As shown in Fig. 5, similar changes occurred in the GHI spectrum. The LI-200 instrument receives a signal from each of the solar wavelengths scaled to the product of the response of the photodiode and the diffuser. The total signal received is the sum of energy received from each of the wavelengths. To measure broadband solar radiation with the LI-200 under current practice, the instrument is calibrated to an airmass of 1.5. This static calibration can produce accurate broadband measurements at other times of the day only if the spectral distribution has the same shape as at calibration time, implying that the proportion of energy in various wavelengths remains the same. As in a Rayleigh scattering environment, shorter wavelengths are preferentially scattered the shape of the spectral distribution, especially in the DNI, which varies (Fig. 4); therefore, we expect that a static calibration for the LI-200 for an airmass of 1.5 will lead to a biased measurement at other times of the day with different airmass. To investigate the impact of spectral shape changes on LI-200 measurement errors, we took the spectral DNI measurements from the PGS-100 instrument and convolved it with the sensor and diffuser spectral responses for a zenith angle of 45 degrees, corresponding approximately to an airmass of 1.5. We then summed the total energy in the convolved calculation for both DNI and GHI to arrive at an estimate of “actual energy” received at the sensor. To create a “calibration” 3 for measuring broadband DNI, we took the broadband measurement from the Kipp and Zonen CH-1 model pyrheliometer (a thermopile instrument) for exactly the same time and location and calculated a ratio of the broadband measurement to the “actual energy” from the PGS-100. We called this the “calibration coefficient,” which was then applied to the convolved spectral sum from the PGS-100 at other times to obtain the broadband solar radiation. A similar method was applied to the spectral GHI measured using the LI-1800. The broadband measurement from the Kipp and Zonen CM-22, a thermopile instrument, was used to compute a similar “calibration coefficient”  at  airmass  1.5  for  silicon devices. Next, we applied the “calibration coefficients” for converting spectral DNI and GHI to the convolved spectral sum for measurements taken at various times on a clear day and compared them to the CH-1 measurements for DNI and CM-22 for GHI. We also took the DNI and GHI measurements from the rotating shadowband radiometer (RSR), which had a LI-200 for measurement, and calculated the differences between the RSR and thermopile instruments. In this experiment, if the differences between the RSR and thermopile instruments were similar to the difference observed in the spectral instrument versus thermopile comparison, we would be able to say definitively that spectral mismatch results in errors in broadband measurements when silicon based instruments are used. 4 RESULTS June 22, 2011, was observed to be a clear day at NREL’s Solar Radiation Research Laboratory in Golden, Colorado, as shown in Fig. 6. The results for the DNI comparison are shown in Fig. 7. The red line in Fig. 7 is our estimate of errors from using a silicon instrument such as the LI-200 because of the change in the energy distribution of the observed spectra. The shape of our estimated errors matched the actual errors from comparing RSR and CH-1 measurements at the same location for the same day.  Fig. 6. The observed DNI from a CH-1 (red) and GHI from a CM-22 (green) for June 22, 2011, shows a clear day.  Fig. 7. Difference in broadband DNI calculated using the spectral DNI and measurements from the CH-1 thermopile instrument are shown by a red line as a function  of  zenith  angle.  Note  that  there  was  no difference between the two at a zenith angle of 45 degrees, the calibration point. The dashed black line shows the observed differences between the measurement using the LI-200 and the CH-1 for the same day. The green line shows the differences if the spectral response of the diffuser was excluded from the spectral “calibration” and calculation. Data used were from June 22, 2011. The errors for high zenith angles were significant and can reach 50 W/m^2. It is notable that the morning and afternoon errors were slightly different, both in our estimates (red) and actual measurements (black). This difference in the errors is attributed to a difference in aerosol loading in the atmosphere, where the spectral distribution is again impacted because of scattering by the aerosol. A closer look at Fig. 4 and Fig. 7 clearly shows that the error grows as the spectral shape changes.  Fig. 8. Difference in broadband GHI (calculated using the spectral DNI difference plus the diffuse difference between the RSR diffuse and thermopile diffuse) is shown by the blue line as a function of zenith angle. The dashed black line  shows the observed differences between the measurement using the LI-200 and the CM-22 for the same day. Data used were from June 22, 2011. In Fig. 8, the blue line shows our theoretical estimate of GHI errors. To calculate the GHI errors, we first calculated diffuse errors using the diffuse measurements from the RSR and the diffuse from a shaded CM-22. We then scaled the estimated DNI difference from Fig. 7, scaled it by the cosine of the solar zenith, and added the diffuse difference to calculate the GHI error estimates. The dotted black lines show the actual errors. It 4 is interesting to note that the errors in GHI were not as high as observed for DNI. This observation was supported by the fact that the spectral GHI in Fig. 5 did not change shape as drastically as the spectral DNI in Fig. 4. This phenomenon can be explained by the fact that the blue light at shorter wavelengths that has been preferentially scattered out of the direct beam forms part of the diffuse radiation that reaches the surface as a component of the GHI. Nevertheless, we found that both GHI and DNI measurements using silicon instruments have errors that are dependent on zenith angle. Other influences were the aerosol loading, as shown in both Fig. 7 and Fig. 8, where the morning and afternoon errors varied because of a change in aerosol loading. 5 SUMMARY We found that broadband measurements using silicon devices deviate from measurements using a thermopile. All measurements were taken using a single-axis tracking platform. As silicon devices have a variable response across the solar spectrum, they are calibrated to broadband thermopile devices at solar zenith angles below 45 degrees, in accordance with protocol. The solar DNI spectrum does not vary uniformly with airmass, because blue light is preferentially scattered out with an increase in airmass; therefore, the calibration coefficient calculated at a particular zenith angle is no longer valid at higher solar zenith angles. This results in over-prediction of broadband solar radiation at higher zenith angles and under-prediction at lower zenith angles. This error must be corrected when determining the absolute efficiency of PV devices. It is expected that similar errors will occur if the calibration coefficient is calculated for a particular environmental condition and the silicon device is deployed in a different environment. As an example, higher aerosol loading will cause similar preferential scattering in the blue part of the solar spectrum and cause similar over-prediction. Additionally, calibrations conducted at higher elevations and low water vapor conditions  will  no  longer  be  applicable  at  lower elevations and humid conditions; therefore, we concluded that the use of silicon devices for PV performance evaluation will lead to uncertainties that cannot easily be quantified. Empirical correction factors have been devised  to  correct  for  the  spectral  errors[4].  Such methods may not be able to provide accurate corrections for diverse conditions seen at various locations. 6 ACKNOWLEDGEMENT This work was supported by the U.S. Department of Energy under Contract No. DE-AC36-08-GO28308 with the National Renewable Energy Laboratory. 7 REFERENCES [1] Stoffel, T.L., et al. Concentrating Solar Power: Best Practices Handbook for the Collection and Use of Solar Resource Data. NREL/TP-550-47465. Golden, CO: National Renewable Energy Laboratory, 2010; 146 pp. Accessed May 9, 2012. www.nrel.gov/docs/fy10osti/47465.pdf. [2] Myers, D.; Stoffel, T.L.; Andreas, A.; Wilcox, S.; Reda, I. Improved Radiometric Calibrations and Measurements for Evaluating Photovoltaic Devices. NREL/TP-520-28941. Golden, CO: National Renewable Energy Laboratory, 2000; 43 pp. Accessed May 9, 2012: http://gisceu.net/PDF/U772.pdf. [3] Solar Radiation Research Laboratory Baseline Measurement System. Accessed May 9, 2012: www.nrel.gov/midc/srrl_bms. [4] King, D.L.; Boyson, W.E.; Hansen, B.R.; Bower, W.I. “Improved Accuracy for Low-Cost Solar Irradiance Sensors.” 2nd World Conference and Exhibition on Photovoltaic Solar Energy Conversion Proceedings; July 6–10, 1998, Vienna, Austria.  

Evaluation of Photodiode and Thermopile Pyranometers for Photovoltaic Applications: Preprint

CiteSeerX

NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 
Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC. 
 
 
Contract No. DE-AC36-08GO28308 
 
  
Evaluation of Photodiode and 
Thermopile Pyranometers for 
Photovoltaic Applications 
Preprint 
M. Sengupta, P. Gotseff, and T. Stoffel 
To be presented at the 27th European Photovoltaic Solar Energy 
Conference and Exhibition 
Frankfurt, Germany 
September 24–28, 2012 
Conference Paper 
NREL/CP-5500-56540 
September 2012 
 
 
NOTICE 
The submitted manuscript has been offered by an employee of the Alliance for Sustainable Energy, LLC 
(Alliance), a contractor of the US Government under Contract No. DE-AC36-08GO28308. Accordingly, the US 
Government and Alliance retain a nonexclusive royalty-free license to publish or reproduce the published form of 
this contribution, or allow others to do so, for US Government purposes. 
This report was prepared as an account of work sponsored by an agency of the United States government. 
Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 
express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 
any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately 
owned rights.  Reference herein to any specific commercial product, process, or service by trade name, 
trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 
or favoring by the United States government or any agency thereof.  The views and opinions of authors 
expressed herein do not necessarily state or reflect those of the United States government or any agency thereof. 
Available electronically at http://www.osti.gov/bridge 
Available for a processing fee to U.S. Department of Energy 
and its contractors, in paper, from: 
U.S. Department of Energy 
Office of Scientific and Technical Information 
P.O. Box 62 
Oak Ridge, TN 37831-0062 
phone:  865.576.8401 
fax: 865.576.5728 
email:  mailto:reports@adonis.osti.gov 
Available for sale to the public, in paper, from: 
U.S. Department of Commerce 
National Technical Information Service 
5285 Port Royal Road 
Springfield, VA 22161 
phone:  800.553.6847 
fax:  703.605.6900 
email: orders@ntis.fedworld.gov 
online ordering:  http://www.ntis.gov/help/ordermethods.aspx 
Cover Photos: (left to right) PIX 16416, PIX 17423, PIX 16560, PIX 17613, PIX 17436, PIX 17721 
 Printed on paper containing at least 50% wastepaper, including 10% post consumer waste. 
1 
EVALUATION OF PHOTODIODE AND THERMOPILE PYRANOMETERS FOR PHOTOVOLTAIC 
APPLICATIONS 
Manajit Sengupta, Peter Gotseff, and Thomas Stoffel 
National Renewable Energy Laboratory 
15013 Denver West Parkway, Golden, CO 80401-3305, USA 
ABSTRACT: Accurately determining photovoltaic (PV) module performance in the field requires measuring solar irradiance 
reaching the PV panel at a high level of accuracy and known uncertainty. Silicon detectors used in various solar energy 
measuring instruments that include reference cells are a potentially attractive choice for multiple reasons, including faster 
responsivity than thermopile detectors, cheaper cost, and lower maintenance. The main drawback, however, is that the silicon 
detectors are only spectrally responsive in a narrow part of the solar spectrum; therefore, to determine broadband solar 
irradiance, a calibration factor that converts the narrowband response to broadband is required. Normally, this calibration factor 
is a single number determined under standard conditions but used for various scenarios, including varying airmass, panel 
orientation, and atmospheric conditions. This would not have been an issue if all wavelengths that form the broadband spectrum 
responded uniformly to atmospheric constituents. Unfortunately, the scattering and absorption signature varies widely across 
wavelengths, and the calibration factor computed under certain test conditions is not appropriate for other conditions. This 
paper lays out the issues that will arise from the use of silicon detectors for PV performance measurement in the field. We 
also present a comparison of simultaneous spectral and broadband measurements from silicon and thermopile detectors and 
estimated measurement errors when using silicon devices for both array performance and resource assessment. 
Keywords: spectral response, photodiodes, pyranometer, direct normal irradiance, global horizontal irradiance 
1 INTRODUCTION 
Silicon photodiode-based pyranometers have been used to 
measure global horizontal irradiance (GHI), primarily in 
agricultural networks, for decades. These radiometers are also 
popular for applications in solar energy conversion. They are 
currently used in numerous locations to measure GHI for 
various purposes, including solar resource assessment and 
photovoltaic (PV) performance [1]. PV performance testing 
requires accurate measurements of both power output by PV 
panels and solar energy incident on the panels (plane-of- array, 
or POA, irradiance). These silicon devices have become popular 
mainly because of their low cost, ease of maintenance, and fast 
time response for high frequency data. Silicon photodiode 
pyranometers provide limited spectral response, as shown in Fig. 
1. The direct normal irradiance (DNI) spectral irradiance shown 
in Fig. 1 illustrates the magnitude of this limitation. The 
calibration of pyranometers is based on simultaneous 
measurements of solar irradiance by a broadband thermopile 
reference (REF) and the unit under test (UUT) [2]. The 
resulting pyranometer responsivity is computed as the ratio 
UUT (µV) / REF (Wm-2). For the photodiode- based 
pyranometer, this calculation represents the energy collected by 
a silicon device to the total energy available in the solar 
spectrum. Calibration data are generally collected throughout 
the day under clear-sky conditions. Unfortunately, the solar 
spectrum does not change uniformly with increasing airmass; 
therefore, the ratio of the energy gathered by a silicon 
photodiode pyranometer compared with the total energy in the 
solar spectrum will vary during the day. This paper seeks to 
understand the impact of this variability and whether the use of 
a constant calibration coefficient results in significant error in 
estimation of broadband POA irradiance using a silicon 
photodiode pyranometer. 
 
Fig. 1. Spectral response function of a silicon photodiode 
pyranometer (in green) shown along with a spectral DNI 
measurement from a summer and winter day (shown in red and 
blue, respectively). 
2 MOTIVATION 
We designed a single-axis tracking device at the National 
Renewable Energy Laboratory (NREL) and mounted multiple 
silicon devices, including a LICOR model LI-200, an Apogee 
model SP 110, an IMT reference cell, as well as a Kipp and 
Zonen model CMP 11 thermopile pyranometer. The goal of this 
instrument package was to investigate the possibility of 
deploying such single-axis tracking devices in the field for solar 
resource assessment relevant to a similarly tracking PV plant 
(Fig. 2). We observed that the silicon-based devices had 
significant measurement difference when compared to each 
other based on the manufacturer’s calibration. We also observed 
2 
that although the LI-200 measurements agreed with the CM 11 
at solar noon, over-prediction of GHI occurred earlier in the 
morning and later in the afternoon. (Fig. 2). The CM 11 
thermopile device was validated against a reference CM 21 at 
solar noon. 
 
Fig. 2. Silicon and thermopile device measurement mounted on 
a single-axis tracker. GHI measurements from a well-calibrated 
instrument are also shown. 
The silicon devices were scaled to the LI-200 using the 
solar noon offset as the correction, as shown in Fig. 3. It was 
clear that all silicon devices agreed with the CM 11 
thermopile instrument at solar noon but over-predicted both in 
the morning and afternoon. 
 
Fig. 3. Silicon and thermopile device measurement mounted on 
a single-axis tracker. The measurements from the silicon devices 
were scaled to the LI-200. GHI measurements from a well-
calibrated instrument are also shown. 
The difference between the silicon and thermopile devices was 
significant enough that over-prediction similar to that observed 
in this case will result in significant errors in PV performance 
evaluation if silicon devices are used. In this paper, we 
investigate the impact of spectral sensitivity of silicon devices 
and whether a static calibration of the silicon devices leads to 
errors in measurement when compared to broadband 
measurements by well-calibrated thermopile devices. 
3 METHODOLOGY AND RESULTS 
The spectral distributions of measured DNI and GHI 
changed as the solar zenith angle changes during the course of 
the day. Fig. 4 shows how the spectral DNI changed during the 
course of a clear day. Fig. 5 shows how the spectral GHI 
changed during the same day. 
 
Fig. 4. Hourly DNI spectra for April 6, 2012, measured from 6 
a.m. to 12 p.m. The dashed black line represents the spectral 
response of the LI-200 instrument. The dotted black line 
represents the spectral response of the diffuser on the LI-200. 
 
Fig. 5. Hourly GHI spectra for April 6, 2012, measured from 
6 a.m. to 12 p.m. The dashed black line represents the spectral 
response of the LI-200 instrument. The dotted black line 
represents the spectral response of the diffuser on the LI-200. 
The spectral DNI was measured using a Kipp and Zonen 
PGS 100 spectrophotometer with the spectra being measured 
from 350 nanometers (nm) to 1050 nm at a resolution of 
approximately 4 nm. A LI-COR LI-1800 spectroradiometer 
measures spectral GHI between the wavelengths of 350 nm and 
1100 nm. 
The DNI spectrum for various times showed that the 
distribution changed during the day as the airmass changed (see 
Fig. 4). As shown in Fig. 5, similar changes occurred in the GHI 
spectrum. The LI-200 instrument receives a signal from each of 
the solar wavelengths scaled to the product of the response of 
the photodiode and the diffuser. The total signal received is 
the sum of energy received from each of the wavelengths. To 
measure broadband solar radiation with the LI-200 under 
current practice, the instrument is calibrated to an airmass of 
1.5. This static calibration can produce accurate broadband 
measurements at other times of the day only if the spectral 
distribution has the same shape as at calibration time, implying 
that the proportion of energy in various wavelengths remains the 
same. As in a Rayleigh scattering environment, shorter 
wavelengths are preferentially scattered the shape of the spectral 
distribution, especially in the DNI, which varies (Fig. 4); 
therefore, we expect that a static calibration for the LI-200 
for an airmass of 1.5 will lead to a biased measurement at other 
times of the day with different airmass. 
To investigate the impact of spectral shape changes on LI-
200 measurement errors, we took the spectral DNI 
measurements from the PGS-100 instrument and convolved it 
with the sensor and diffuser spectral responses for a zenith angle 
of 45 degrees, corresponding approximately to an airmass of 
1.5. We then summed the total energy in the convolved 
calculation for both DNI and GHI to arrive at an estimate of 
“actual energy” received at the sensor. To create a “calibration” 
3 
for measuring broadband DNI, we took the broadband 
measurement from the Kipp and Zonen CH-1 model 
pyrheliometer (a thermopile instrument) for exactly the same 
time and location and calculated a ratio of the broadband 
measurement to the “actual energy” from the PGS-100. We 
called this the “calibration coefficient,” which was then applied 
to the convolved spectral sum from the PGS-100 at other times 
to obtain the broadband solar radiation. A similar method was 
applied to the spectral GHI measured using the LI-1800. The 
broadband measurement from the Kipp and Zonen CM-22, a 
thermopile instrument, was used to compute a similar 
“calibration coefficient”  at  airmass  1.5  for  silicon devices. 
Next, we applied the “calibration coefficients” for 
converting spectral DNI and GHI to the convolved spectral sum 
for measurements taken at various times on a clear day and 
compared them to the CH-1 measurements for DNI and CM-22 
for GHI. We also took the DNI and GHI measurements from the 
rotating shadowband radiometer (RSR), which had a LI-200 
for measurement, and calculated the differences between the 
RSR and thermopile instruments. In this experiment, if the 
differences between the RSR and thermopile instruments were 
similar to the difference observed in the spectral instrument 
versus thermopile comparison, we would be able to say 
definitively that spectral mismatch results in errors in broadband 
measurements when silicon based instruments are used. 
4 RESULTS 
June 22, 2011, was observed to be a clear day at NREL’s 
Solar Radiation Research Laboratory in Golden, Colorado, as 
shown in Fig. 6. The results for the DNI comparison are shown 
in Fig. 7. 
The red line in Fig. 7 is our estimate of errors from using a 
silicon instrument such as the LI-200 because of the change in 
the energy distribution of the observed spectra. The shape of 
our estimated errors matched the actual errors from comparing 
RSR and CH-1 measurements at the same location for the same 
day. 
 
Fig. 6. The observed DNI from a CH-1 (red) and GHI from a 
CM-22 (green) for June 22, 2011, shows a clear day. 
 
Fig. 7. Difference in broadband DNI calculated using the 
spectral DNI and measurements from the CH-1 thermopile 
instrument are shown by a red line as a function  of  zenith  
angle.  Note  that  there  was  no difference between the two at a 
zenith angle of 45 degrees, the calibration point. The dashed 
black line shows the observed differences between the 
measurement using the LI-200 and the CH-1 for the same day. 
The green line shows the differences if the spectral response 
of the diffuser was excluded from the spectral “calibration” and 
calculation. Data used were from June 22, 2011. 
The errors for high zenith angles were significant and can 
reach 50 W/m^2. It is notable that the morning and afternoon 
errors were slightly different, both in our estimates (red) and 
actual measurements (black). This difference in the errors is 
attributed to a difference in aerosol loading in the atmosphere, 
where the spectral distribution is again impacted because of 
scattering by the aerosol. A closer look at Fig. 4 and Fig. 7 
clearly shows that the error grows as the spectral shape changes. 
 
Fig. 8. Difference in broadband GHI (calculated using the 
spectral DNI difference plus the diffuse difference between the 
RSR diffuse and thermopile diffuse) is shown by the blue line as 
a function of zenith angle. The dashed black line  shows the 
observed differences between the measurement using the LI-
200 and the CM-22 for the same day. Data used were from June 
22, 2011. 
In Fig. 8, the blue line shows our theoretical estimate of GHI 
errors. To calculate the GHI errors, we first calculated diffuse 
errors using the diffuse measurements from the RSR and the 
diffuse from a shaded CM-22. We then scaled the estimated 
DNI difference from Fig. 7, scaled it by the cosine of the solar 
zenith, and added the diffuse difference to calculate the GHI 
error estimates. The dotted black lines show the actual errors. It 
4 
is interesting to note that the errors in GHI were not as high as 
observed for DNI. This observation was supported by the fact 
that the spectral GHI in Fig. 5 did not change shape as 
drastically as the spectral DNI in Fig. 4. This phenomenon can 
be explained by the fact that the blue light at shorter 
wavelengths that has been preferentially scattered out of the 
direct beam forms part of the diffuse radiation that reaches the 
surface as a component of the GHI. 
Nevertheless, we found that both GHI and DNI 
measurements using silicon instruments have errors that are 
dependent on zenith angle. Other influences were the aerosol 
loading, as shown in both Fig. 7 and Fig. 8, where the morning 
and afternoon errors varied because of a change in aerosol 
loading. 
5 SUMMARY 
We found that broadband measurements using silicon 
devices deviate from measurements using a thermopile. All 
measurements were taken using a single-axis tracking platform. 
As silicon devices have a variable response across the solar 
spectrum, they are calibrated to broadband thermopile devices at 
solar zenith angles below 45 degrees, in accordance with 
protocol. The solar DNI spectrum does not vary uniformly with 
airmass, because blue light is preferentially scattered out with an 
increase in airmass; therefore, the calibration coefficient 
calculated at a particular zenith angle is no longer valid at higher 
solar zenith angles. This results in over-prediction of broadband 
solar radiation at higher zenith angles and under-prediction at 
lower zenith angles. This error must be corrected when 
determining the absolute efficiency of PV devices. It is expected 
that similar errors will occur if the calibration coefficient is 
calculated for a particular environmental condition and the 
silicon device is deployed in a different environment. As an 
example, higher aerosol loading will cause similar preferential 
scattering in the blue part of the solar spectrum and cause 
similar over-prediction. Additionally, calibrations conducted at 
higher elevations and low water vapor conditions  will  no  
longer  be  applicable  at  lower elevations and humid 
conditions; therefore, we concluded that the use of silicon 
devices for PV performance evaluation will lead to uncertainties 
that cannot easily be quantified. Empirical correction factors 
have been devised  to  correct  for  the  spectral  errors[4].  Such 
methods may not be able to provide accurate corrections for 
diverse conditions seen at various locations. 
6 ACKNOWLEDGEMENT 
This work was supported by the U.S. Department of Energy 
under Contract No. DE-AC36-08-GO28308 with the National 
Renewable Energy Laboratory. 
7 REFERENCES 
[1] Stoffel, T.L., et al. Concentrating Solar Power: Best 
Practices Handbook for the Collection and Use of Solar 
Resource Data. NREL/TP-550-47465. Golden, CO: National 
Renewable Energy Laboratory, 2010; 146 pp. Accessed May 9, 
2012. www.nrel.gov/docs/fy10osti/47465.pdf. 
[2] Myers, D.; Stoffel, T.L.; Andreas, A.; Wilcox, S.; Reda, I. 
Improved Radiometric Calibrations and Measurements for 
Evaluating Photovoltaic Devices. NREL/TP-520-28941. 
Golden, CO: National Renewable Energy Laboratory, 2000; 43 
pp. Accessed May 9, 2012: http://gisceu.net/PDF/U772.pdf. 
[3] Solar Radiation Research Laboratory Baseline Measurement 
System. Accessed May 9, 2012: www.nrel.gov/midc/srrl_bms. 
[4] King, D.L.; Boyson, W.E.; Hansen, B.R.; Bower, W.I. 
“Improved Accuracy for Low-Cost Solar Irradiance Sensors.” 
2nd World Conference and Exhibition on Photovoltaic Solar 
Energy Conversion Proceedings; July 6–10, 1998, Vienna, 
Austria. 
 


Evaluation of Photodiode and Thermopile Pyranometers for Photovoltaic Applications

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Evaluation of Photodiode and Thermopile Pyranometers for Photovoltaic Applications

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