•Extracted outdoor Suns-VOC parameters fall within 1% of indoor lab measurements•Weather station data proved as a scalable alternative to individual sensors•Suns-VOC parameters can be extracted at low irradiance periods (i.e., sunrise)•This method is resilient to instances of partial shading or cloud coverage Photovoltaics (PVs) have rapidly grown due to advancements in efficiency and cost. PV is projected to increase to 48% of all renewable generation by 2050, making it the fastest growing source of energy generation. More emphasis has been placed on reliability, as a path to reducing LCOE by improving degradation rates and system lifespans. We capitalize on Suns-VOC, which is widely used for laboratory measurements of single solar cells, and discuss the barriers in extending the technique to outdoor systems of all sizes. The resulting data can provide a thorough analysis of impeding faults and degradation. Because Suns-VOC is rather simple to implement on fielded systems, it can be a valuable tool for collecting the data needed to better understand how degradation mechanisms and climates impact different PV architectures. This work provides a scalable pathway to provide the industry with the information needed to achieve PV lifespans of beyond 50 years. In-field characterization of photovoltaics is crucial to understand performance and degradation mechanisms, subsequently improving overall reliability and lifespans. Current outdoor characterization is limited by logistical difficulties, variable weather, and requirements to measure during peak production hours. We capitalize on Suns-VOC, which is widely used for laboratory measurements of single solar cells and discuss the barriers in extending the technique to outdoor systems. We demonstrate the normalization of measurements using both backsheet temperature sensors and on-site weather stations. Despite weather variation, VOC, ideality factor, and pseudo fill factor all fall within 1% of the laboratory measurements. It is also demonstrated that monitoring the system VOC at 0.05 to 0.1 suns, during minimal power production, provides a figure of merit that can indicate early degradation of the system. Extensive simulations show that shading portions of a system has minimal effect on measurements, allowing the technique to be used in all weather conditions. In-field characterization of photovoltaics is crucial to understand performance and degradation mechanisms, subsequently improving overall reliability and lifespans. Current outdoor characterization is limited by logistical difficulties, variable weather, and requirements to measure during peak production hours. We capitalize on Suns-VOC, which is widely used for laboratory measurements of single solar cells and discuss the barriers in extending the technique to outdoor systems. We demonstrate the normalization of measurements using both backsheet temperature sensors and on-site weather stations. Despite weather variation, VOC, ideality factor, and pseudo fill factor all fall within 1% of the laboratory measurements. It is also demonstrated that monitoring the system VOC at 0.05 to 0.1 suns, during minimal power production, provides a figure of merit that can indicate early degradation of the system. Extensive simulations show that shading portions of a system has minimal effect on measurements, allowing the technique to be used in all weather conditions. Maintaining high performance fielded photovoltaic (PV) systems requires adequate and informative characterization tools. In-field characterization methods are an essential part of performance monitoring,1Rezk H. Tyukhov I. Al-Dhaifallah M. Tikhonov A. Performance of data acquisition system for monitoring PV system parameters.Measurement. 2017; 104: 204-211Crossref Scopus (57) Google Scholar system diagnostics/detection, and attribution of premature component failure.2Firth S.K. Lomas K.J. Rees S.J. A simple model of PV system performance and its use in fault detection.Sol. Energy. 2010; 84: 624-635Crossref Scopus (154) Google Scholar Monitoring changes in power output has historically been used to gauge levels of degradation, but it fails to provide insight on specific loss mechanisms.3French R.H. Podgornik R. Peshek T.J. Bruckman L.S. Xu Y. Wheeler N.R. Gok A. Hu Y. Hossain M.A. Gordon D.A. et al.Degradation science: mesoscopic evolution and temporal analytics of photovoltaic energy materials.Curr. Opin. Solid State Mater. Sci. 2015; 19: 212-226Crossref Scopus (45) Google Scholar,4Meyer E.L. van Dyk E.E. Assessing the reliability and degradation of photovoltaic module performance parameters.IEEE Trans. Rel. 2004; 53: 83-92Crossref Scopus (274) Google Scholar Indoor Suns-VOC has been used extensively to identify and quantify loss mechanisms as well as identify the early onset of losses such as resistive shunts5Sinton, R.A., and Cuevas, A. (2000). A Quasi-steady-state open-circuit voltage method for solar cell characterization. Proceedings of the 16th European Photovoltaic Solar Energy Conference, pp. 1–4.Google Scholar during reliability testing. In this work, we demonstrate the scalability of using outdoor Suns-VOC as a complementary or alternative characterization technique for monitoring modules and arrays, requiring minimal hardware, and utilizing the sun as the illumination source without impeding power production. The implications of this work can be used to better understand the operation of systems of all sizes, ranging from small residential systems to larger powerplant sized systems. We also demonstrate that this method is robust for systems operating under partial shading conditions. At the end of 2019, 650 GW of PV was installed throughout the world, representing over 2 billion panels operating in multiple climates with varying weather conditions.6Jäger-Waldau A. PV Status Report 2019. EUR 29938 EN. Publications Office of the European Union, 2019https://op.europa.eu/en/publication-detail/-/publication/dfa5cde5-05c6-11ea-8c1f-01aa75ed71a1/language-enGoogle Scholar In 2024, projections show this number doubling. For many of these systems, the only available performance metrics are based on power output measurements. Small, distributed systems of heterogeneous design and configuration may not be well characterized enough for inclusion in large fleet-scale performance datasets.7Meyers B. Deceglie M. Deline C. Jordan D. Signal processing on PV time-series data: robust degradation analysis without physical models.IEEE J. Photovoltaics. 2020; 10: 546-553Crossref Scopus (9) Google Scholar Power-based performance metrics are clearly insufficient given variable weather conditions and high DC to AC ratios, and they do not provide insight into specific mechanisms for power decline. Small and large systems alike would benefit from scalable, real-time performance metrics more robust to variable conditions that provide mechanistic insight into system performance, degradation, and failures.8Munoz M.A. Alonso-García M.C. Vela N. Chenlo F. Early degradation of silicon PV modules and guaranty conditions.Sol. Energy. 2011; 85: 2264-2274Crossref Scopus (239) Google Scholar PV systems may suffer from various degradation mechanisms, influenced by cell and module architecture, installation, and local weather and climate. Some common degradation mechanisms, which can be evaluated and attributed with Suns-VOC or the extracted series resistance (RS) from Suns-VOC,9Deceglie M.G. Silverman T.J. Marion B. Kurtz S.R. Real-time series resistance monitoring in PV systems without the need for I-V curves.IEEE J. Photovoltaics. 2015; 5: 1706-1709Crossref Scopus (12) Google Scholar are listed in Table 1. Suns-VOC, especially if complemented with other characterization, can provide attribution of degradation mechanisms that affect system current-voltage (I-V) properties, including short circuit current (ISC), open-circuit voltage (VOC), RS, shunt resistance (RSH), etc.Table 1Detection of Common Degradation Mechanisms of Fielded PV ModulesDegradation MechanismDegradation CauseResulting PerformancePotential Characterization MethodSolder joint failureThermalRS increaseRS from Suns-VOC9Deceglie M.G. Silverman T.J. Marion B. Kurtz S.R. Real-time series resistance monitoring in PV systems without the need for I-V curves.IEEE J. Photovoltaics. 2015; 5: 1706-1709Crossref Scopus (12) Google ScholarDelamination/discoloration (resulting in corrosion)Moisture, Thermal, UVISC reduction, RS increasePhysical Inspection/RS from Suns-VOC9Deceglie M.G. Silverman T.J. Marion B. Kurtz S.R. Real-time series resistance monitoring in PV systems without the need for I-V curves.IEEE J. Photovoltaics. 2015; 5: 1706-1709Crossref Scopus (12) Google ScholarLIDIrradianceISC & VOC reductionSuns-VOC10Fertig F. Krauß K. Rein S. Light-induced degradation of PECVD aluminium oxide passivated silicon solar cells.Phys. Status Solidi RRL. 2015; 9: 41-46Crossref Scopus (117) Google ScholarLeTIDIrradiance, TthermalISC & VOC reductionSuns-VOC11Chen R. Tong H. Zhu H. Ding C. Li H. Chen D. Hallam B. Chong C.M. Wenham S. Ciesla A. 83% efficient mono-PERC incorporating advanced hydrogenation.Prog. Photovolt. Res. Appl. 2020; 23: 1-9Google ScholarPIDVoltageISC & RSH reductionSuns-VOC12Wilterdink H. Sinton R. Hacke P. Terwilliger K. Meydbray J. Monitoring the recovery of c-Si modules from potential-induced degradation using suns-voc curves.IEEE 43rd Photovoltaic Specialists Conference (PVSC). 2016; : 2752-2755Crossref Scopus (3) Google Scholar Open table in a new tab Using Suns-VOC, many of the most common degradation mechanisms can be detected. Most of the referenced work in Table 1 uses indoor Suns-VOC measurements at the cell level. Outdoor Suns-VOC measurements on large systems have the potential to provide the same metrics as indoor Suns-VOC, enabling observations of these same degradation mechanisms in fielded modules. Suns-VOC offers an alternative to power-based performance data and is relatively simple to implement on systems of all sizes. We believe that by implementing Suns-VOC on outdoor systems, the PV community can begin collecting more datasets on systems of different sizes, cell types, and climates, effectively providing the data needed for more thorough reliability and degradation analyses. Current techniques of acquiring data tend to only provide data at maximum power points and can pose difficulties for scaling to larger plants. I-V curves are a common method for in-field characterization of diode properties but can create logistical difficulties in large fields, such as disconnecting and reconnecting modules, or acquiring enough measurements to provide an accurate statistical representation of the overall system. These logistical difficulties interrupt power production during measurements and prevent scalability. Novel techniques, such as characterization using aerial imaging, provide scalable alternatives to the logistical complications of I-V measurements, but these methods are not able to measure circuit parameters.13Tsanakas, J.A., Vannier, G., Plissonnier, A., Ha, D.L., and Barruel, F. (2015). Fault diagnosis and classification of large-scale photovoltaic plants through aerial orthophoto thermal mapping. Proceedings of the 31st European Photovoltaic Solar Energy Conference and Exhibition, pp. 1783–1788.Google Scholar Furthermore, power or I-V analysis is typically restricted to unshaded, clear sky periods, meaning that data from systems with persistent shade or poor prevailing irradiance are either excluded from large fleet-scale analyses14Jordan D.C. Deline C. Kurtz S.R. Kimball G.M. Anderson M. Robust PV degradation methodology and application.IEEE J. Photovoltaics. 2018; 8: 525-531Crossref Scopus (82) Google Scholar or requires statistical translations of data from shaded conditions to hypothetical clear sky conditions.7Meyers B. Deceglie M. Deline C. Jordan D. Signal processing on PV time-series data: robust degradation analysis without physical models.IEEE J. Photovoltaics. 2020; 10: 546-553Crossref Scopus (9) Google Scholar Here, we present a direct measurement, Suns-VOC, that is relatively simple to collect, can be collected at times of low irradiance, and is robust to partial shading. Suns-VOC has previously been demonstrated outdoors on modules and arrays,15Killam A. Bowden S. Characterization of modules and arrays with suns Voc.IEEE 44th Photovoltaic Specialist Conference (PVSC). 2017; : 2719-2722Crossref Scopus (0) Google Scholar, 16Forsyth M.K. Mahaffey M. Blum A.L. Dobson W.A. Sinton R.A. Use of the Suns-Voc for diagnosing outdoor arrays amp; modules.IEEE 40th Photovoltaic Specialist Conference (PVSC). 2014; 2014: 1928-1931Crossref Scopus (15) Google Scholar, 17Guo, S., Schneller, E., Walters, J., Davis, K.O., and Schoenfeld, W.V. (2016). Detecting loss mechanisms of c-Si PV modules in-situ I-V measurement. In Reliability of Photovoltaic Cells, Modules, Components, and Systems IX Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series., pp. 99380N.Google Scholar, 18Walters, J., Guo, S., Schneller, E., Seigneur, H., and Boyd, M. (2018). PV module loss analysis using system in-situ monitoring data. IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC 34th EU PVSEC), pp. 2204–2208.Google Scholar but without details regarding day-to-day variation and impacts from uncontrollable weather. Variations include transient, diurnal, and seasonal effects like cloud coverage,19Nann S. Riordan C. Solar spectral irradiance under overcast skies (solar cell performance effects).IEEE Conference on Photovoltaic Specialists. 1990; 2: 1110-1115Crossref Google Scholar temperature changes,20Nordmann, T., and Clavadetscher, L. (2003). Understanding temperature effects on PV system performance. 3rd World Conference onPhotovoltaic Energy Conversion, 2003. Proceedings of, Osaka 3, 2243–2246.Google Scholar wind,21Kaldellis J.K. Kapsali M. Kavadias K.A. Temperature and wind speed impact on the efficiency of PV installations. Experience obtained from outdoor measurements in Greece.Renew. Energy. 2014; 66: 612-624Crossref Scopus (150) Google Scholar angle-of-incidence changes,22King, D.L., Kratochvil, J.A., and Boyson, W.E. (1997). Measuring solar spectral and angle-of-incidence effects on photovoltaic modules and solar irradiance sensors. Conference Record of the Twenty Sixth IEEE Photovoltaic Specialists Conference, pp. 1113–1116.Google Scholar spectral effects,23Dirnberger D. Blackburn G. Müller B. Reise C. On the impact of solar spectral irradiance on the yield of different PV technologies.Sol. Energy Mater. Sol. Cells. 2015; 132: 431-442Crossref Scopus (120) Google Scholar albedo,24Andrews R.W. Pearce J.M. The effect of spectral albedo on amorphous silicon and crystalline silicon solar photovoltaic device performance.Sol. Energy. 2013; 91: 233-241Crossref Scopus (63) Google Scholar and soiling.25Mani M. Pillai R. Impact of dust on solar photovoltaic (PV) performance: research status, challenges and recommendations.Renew. Sustain. Energy Rev. 2010; 14: 3124-3131Crossref Scopus (568) Google Scholar Robust characterization techniques must be able to normalize data obtained in changing conditions. Temperature is the most crucial variable in this study due to the drastic impact it has on PV’s VOC. Temperature variation is also the biggest difference between indoor and outdoor measurements, and the ability to correct for temperature enables the use of the existing literature base in Suns-VOC. In this work, we show how to use Suns-VOC in outdoor conditions, by normalizing the temperature effects during measurements using both backsheet and ambient temperature. Irradiance effects, such as spectrum shifts, are of secondary importance since the VOC varies with the logarithm of the light intensity, and the irradiance sensor has the same characteristics as the array. Indeed, we show below that the gross effect of shading has a minimal effect on the Suns-VOC measurements. Suns-VOC allows the construction of a pseudo I-V curve (Figure 1), which is equivalent to the standard I-V curve described above but without the effects of RS.5Sinton, R.A., and Cuevas, A. (2000). A Quasi-steady-state open-circuit voltage method for solar cell characterization. Proceedings of the 16th European Photovoltaic Solar Energy Conference, pp. 1–4.Google Scholar The measurement provides estimations of recombination-related parameters that limit the fill factor (FF), which are difficult to discern from light I-V curves alone.26Bowden, S.B., Yelundur, V.N., and Rohatgi, A. (2002). Implied-Voc and suns-Voc measurements in multicrystalline solar cells. Proceedings of the 29th IEEE Photovoltaic Special Conference, pp. 371–374.Google Scholar,27Bowden, S., and Rohatgi, A. (2001). Rapid and accurate determination of series resistance and fill factor losses in industrial silicon solar cells. Proceedings of the of the 17th European Photovoltaic Solar Energy conference, pp. 1802–1805.Google Scholar Suns-VOC provides the following parameters: the Suns-VOC curve maximum power point and fill factor, i.e., pseudo max power and pseudo FF (pPMP and pFF), two-diode parameters fitted to the Suns-VOC curve (J01 and J02), and diode ideality factor (n) as a function of cell operating point. In addition to the parameters derived directly from the Suns-VOC curve in the previous paragraph, comparing the light I-V curve with the Suns-VOC curve (Figure 1) enables the accurate measurement of RS, given by Equation 1:RS=ΔVI,(Equation 1) where ΔV is the voltage difference between the corresponding Suns-VOC and the light I-V curve, and I is the current. In this paper, we present VOC at 1 and 0.1 suns, pPMP, and pFF, but other parameters might also be of interest, particularly for attribution of degradation mechanisms.28Karas J. Sinha A. Buddha V.S.P. Li F. Moghadam F. Tamizhmani G. Bowden S. Augusto A. Damp heat induced degradation of silicon heterojunction solar cells With Cu-plated contacts.IEEE J. Photovolt. 2019; 10: 153-158Crossref Scopus (12) Google Scholar Indoor Suns-VOC uses a slowly varying-intensity light source to excite the cell over several orders of magnitude of irradiance.29Kerr M.J. Cuevas A. Sinton R.A. Generalized analysis of quasi-steady-state and transient decay open circuit voltage measurements.J. Appl. Phys. 2002; 91: 399-404Crossref Scopus (113) Google Scholar The irradiance is measured in Suns, a convenience unit to describe fractions of 1 kW/m2. Outdoors, the diurnal changes in the solar insolation (including just before sunrise and just after sunset) provide the required changes in light intensity. The most critical point in a Suns-VOC measurement corresponds to the maximum power point (MPP) on the one-sun I-V power curve. Since the voltage across the diode is the same in both cases, the excess carrier concentrations are roughly equivalent, even though the Suns-VOC measurement is at open-circuit while the MPP is under load. The equivalence in operating points means they have the same levels of recombination, and quantifying losses in the Suns-VOC measurement applies directly to the IV curve. The illumination in suns required to capture information about MPP, Suns(MPP) is related to one-sun ISC and IMP by Equation 2:Suns(MPP)=ISC−IMPISC(Equation 2) Based on the CEC database,30Blair N. DiOrio N. Freeman J. Gilman P. Janzou S. Neises T. Wagner M. System advisor model (SAM) general description (Version 2017.9.5).https://www.nrel.gov/docs/fy18osti/70414.pdfDate: 2018Google Scholar the Suns(MPP) for roughly 95% of commercial silicon modules falls within the range of 0.05 to 0.1 suns. Therefore, the most valuable Suns-VOC data are during low-illumination periods of up to 0.1 suns, where the impact on system output is low. The Suns(MPP) is likely to change slightly over time, which is a result of ISC and/or IMP degradation. A study of degradation rates of 12 different silicon modules found most severe ISC and IMP degradation of 0.71% and 0.89% annually.31Smith, R.M., Jordan, D.C., and Kurtz, S.R. (2012). Outdoor PV module degradation of current-voltage parameters. Proceedings of the 2012 World Renewable Energy Forum., pp. 1–7.Google Scholar These degradation rates correspond to a Suns(MPP) increase of approximately 0.002 suns per year, or 0.05 suns over 25 years, for most modules in the CEC database. Therefore, Suns-VOC might need to be monitored slightly beyond Suns(MPP) to account for possible future degradation. The simplest implementation is to measure temperature-corrected array VOC from 0 suns to at least Suns(MPP) and tracking it over time. This allows for monitoring the array before the start-up voltage for most inverters. Another simple implementation would be to track temperature-corrected DC voltage at maximum power (VMP) against incident irradiance (Suns-VMP).32Sun X. Chavali R.V.K. Alam M.A. Real-time monitoring and diagnosis of photovoltaic system degradation only using maximum power point-the Suns-Vmp method.Prog. Photovolt. Res. Appl. 2019; 27: 55-66Crossref Scopus (21) Google Scholar This technique allows for data collection during normal MPP-tracked system operation but does not remove the RS contribution from resulting pseudo I-V curves. Comparison of Suns-VMP and Suns-VOC data allows RS estimation and more advanced degradation analysis. The operating temperature of a PV module undergoes rapid changes during sunrise and sunset, which corresponds to the light intensities of interest. For accurate temperature normalization, these temperature changes need to be accurately monitored, with special consideration given to the spatial distribution of module temperature. Non-uniform irradiance, wind conditions, and unmatched cell efficiencies may cause non-uniform temperature distribution.33Zhou J. Yi Q. Wang Y. Ye Z. Temperature distribution of photovoltaic module based on finite element simulation.Sol. Energy. 2015; 111: 97-103Crossref Scopus (92) Google Scholar The backsheet temperature was measured and used to calculate the cell temperature via Equation 3.34King D.L. Boyson E.W. Kratochvil J.A. Photovoltaic array performance model.http://www.mauisolarsoftware.com/MSESC/xPerfModel2003.pdfDate: 2004Google Scholar The VOC was then normalized to a specific temperature using Equation 4.35Wang M. Liu J. Burleyson T.J. Schneller E.J. Davis K.O. French R.H. Braid J.L. Analytic Isc–Voc method and power loss modes From outdoor time-series I–V curves.IEEE J. Photovolt. 2020; 10: 1379-1388Crossref Scopus (5) Google Scholar Both Equations 3 and 4 are examined in more detail under the Experimental Procedures section.TCell=TBacksheet+(Suns×3°C)(Equation 3) Voc=β0+β1∗ln(Suns)+β2∗T(Equation 4) Partial to near-complete shading complicates the analysis of module and array I-V curves. Bypass diodes create “stepped” I-V curves in partial shade; these stepped curves are typically filtered out when performing long-term analysis on large I-V curve datasets.36Ma X. Huang W. Schnabel E. Köhl M. Brynjarsdóttir J. Braid J.L. French R.H. Data-driven I–V feature extraction for photovoltaic modules.IEEE J. Photovolt. 2019; 9: 1405-1412Crossref Scopus (22) Google Scholar Here, we use LTspice (Linear Technology Corp) circuit modeling to demonstrate the stability of Suns-VOC curves obtained from varying partial shade conditions that would otherwise generate erratic stepped I-V curves.37Guo S. Ma F.-J. Hoex B. Aberle A.G. Peters M. Analysing solar cells by circuit modelling.Energy Procedia. 2012; 25: 28-33Crossref Scopus (9) Google Scholar A model was built of a 96-cell solar module comparable with those used in outdoor experimentation, as shown in Table 2. Individual PV cells were simulated based on a single-diode model, using RS and RSH values of 0.01 and 300 Ω, respectively.38Khanna V. Das B.K. Bisht D. MATLAB/SIMELECTRONICS models based study of solar cells.Int. J. Renew. Energy Res. 2013; 3: 30-34Google Scholar Both I-V and Suns-VOC measurements were simulated with various shading scenarios. Figure 2 shows modeled light I-V and Suns-VOC curves for one-sun irradiance with 50% partial shading affecting one, two, and three cells, each from a different string.Table 2STC Ratings of BSM230 PV ModulesModule Electrical ParameterNominal ValuesPMP230 (± 5%) WIMP4.82 (± 5%) AVMP48.05 (± 5%) VISC5.23 (± 5%) AVOC58.6 (± 5%) V Open table in a new tab The difference between the I-V and Suns-VOC curves with zero shading is a manifestation of RS. The steps found within the shaded simulations for the light I-V curve represent the activation of the bypass diodes. Bypass diodes are used in modules to prevent the formation of hot spots during periods of partial shade.39Silvestre S. Boronat A. Chouder A. Study of bypass diodes configuration on PV modules.Appl. Energy. 2009; 86: 1632-1640Crossref Scopus (394) Google Scholar The bypass diodes only minimally impact the Suns-VOC curves. When comparing the fully illuminated model to that of the three shaded cells, the pPMP dropped by 0.2%, whereas the light I-V PMP dropped by 35.5%. To further explore the stability of Suns-VOC in widely varying partial shade conditions, we extend the 96-cell module LTspice model to include many different partial shade scenarios. These scenarios were created to demonstrate a wide range of possible partial shade conditions but are not necessarily reflective of real-world partial shading for a typical system. Figure 3 shows frequency histograms of the PMP from I-V and pPMP from Suns-VOC, from roughly 500 different partial shade scenarios. Scenarios include shading affecting two of the three strings, from 0% (completely shaded) to 100% (fully illuminated), whereas string three is held at fully illuminated conditions. The x axis of Figure 3 corresponds to the shading level on the most shaded of the three strings. As seen on the left side of Figure 3, PMP varies substantially due to bypass diode activation and reduced light-generated current. For the given shading scenarios, the average of PMP is 126 W with a standard deviation of 40.5 W. The right half of Figure 3 shows that Suns-VOC pPMP falls within a much more tightly distributed range, with an average of 238 W and a standard deviation of 25.7 W. Suns-VOC provides a pPMP within approximately 5% of the unshaded pPMP when all strings are illuminated at values greater than ∼5%. These results suggest that in systems that regularly operate in partial shade, I-V curves, or time-series PMP data might rarely contain useful performance information. Considering diffuse irradiance is typically well over 5% of total illumination,40Kurtz S.R. Myers D. Townsend T. Whitaker C. Maish A. Hulstrom R. Emery K. Outdoor rating conditions for photovoltaic modules and systems.Sol. Energy Mater. Sol. Cells. 2000; 62: 379-391Crossref Scopus (25) Google Scholar scenarios of less than 5% absolute illumination anywhere on the module are rare, even during periods of major shading. While true pPMP requires uniform irradiance, a close estimate of pPMP is obtained under almost all irradiance and shading conditions. Suns-VOC curves frequently provide a value of pPMP within 5% of the true unshaded pPMP, providing a basis for robust time-series performance monitoring. Outdoor Suns-VOC data for a single-cell module are shown in Figure 4, with translations to 10°C, 25°C, and 40°C via Equation 4. Indoor Suns-VOC measurements were also conducted at these respective temperatures using a Sinton FCT-450 cell flash tester; these curves are also displayed in Figure 4. Temperatures of 25°C are used for standard test condition (STC) measurements, whereas 10°C and 40°C are used to demonstrate translations to lower and higher operating temperatures. Measured VOC datapoints are colored by cell temperature as determined from backsheet temperature measurements and Equation 3. Normalizing the measured outdoor data via Equation 4 to 10°C, 25°C, and 40°C yields an excellent agreement with the indoor curves. Suns-VOC parameters for each outdoor temperature translation are shown in Table 3, with the respective indoor parameters at the same temperature.Table 3Suns-VOC Parameters on a Single-Cell Module for Both Indoor and Normalized Outdoor MeasurementsRS (Ω-cm2)VOC at 1 Sun (V)VOC at 0.1 Suns (V)npFFpPMP (W)10°CIndoor2.890.6650.6031.000.8344.71Outdoor2.720.6650.5991.020.8314.7025°CIndoor2.950.6290.5631.110.8214.39Outdoor2.960.6300.5641.120.8204.3940°CIndoor3.010.5890.5191.270.8034.02Outdoor3.020.5870.5201.250.8064.02 Open table in a new tab The pPMP values of the temperature-translated outdoor Suns-VOC curves are within 0.04% of the respective indoor Suns-VOC curves, indicating the validity of our outdoor measurement setup and VOC translations via Equation 4. Slightly larger percentage differences occur when translating to 10°C. This is likely due to the paucity of data at such low temperatures for generating fit coefficients, considering the average outdoor operating temperature was approximately 35°C. Translating data to temperatures closer to the average operating temperature results in a more accurate fit. The specified translation temperature is best chosen given the average operating temperature of a given site or season. The measurement error when comparing outdoor to indoor measurements must be less than the percentage of expected degradation to ensure viability. Modules are typically warrantied for ∼1% degradation of maximum power per year. The measurement error for outdoor compared with indoor measurements equate to less than 0.04% error for pPMP, when analyzing using 25°C and 40°C for temperature translations. Because the measurement error is significantly less than the typical warrantied degradation, one should be able to use these data to make a reasonable assumption rega
Search from 250 M+ research papersSearch from 250 M+ research papers
Open Access
Nov 1, 2023
George Kattawar + 1