Thermal and electrical effects of partial shade in monolithic thin-film photovoltaic modules

Silverman, Timothy J.; Deceglie, Michael G.; Sun, Xingshu; Garris, Rebekah L.; Alam, Muhammad Ashraful; Deline, Chris; Kurtz, Sarah

doi:10.1109/pvsc.2015.7355668

Cited by 13 publications

(28 citation statements)

References 8 publications

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“…Electron-beam- Table 2 and were taken from previous studies. 3,7,8 3 | CHARACTERIZATION OF REVERSE-BIAS BREAKDOWN SITES…”

Section: Methodsmentioning

confidence: 99%

“…As shown in Figure 10, the ignition event, which can be hot enough to melt CIGS, occurs within a few seconds, as suggested by our DLIT images and other observations. 1,3,4,10 The melting point of CIGS ranges from 1260 to 1340 K depending on the Ga content. 12 There is also signifi- Figure 11A,B.…”

Section: Thermal Runawaymentioning

confidence: 99%

“…The single bypass diode used for the entire module will not be acti- The damage that occurs under partial shading, or reverse-bias, conditions is often optically visible after a short time period and has the appearance of "worm trail" or "wormlike" features. [1][2][3] These wormlike defects have been observed to form under reverse-bias conditions whether or not the module or cells are illuminated. 4 Although inclusions and voids have been found to be related to the formation of reverse-bias damage in CIGS devices, 5 there is a degree of uncertainty in the cause and spatial distribution of the locations where thermal runaway breakdown is initiated.…”

Section: Introductionmentioning

confidence: 99%

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Characterization and modeling of reverse‐bias breakdown in Cu(In,Ga)Se₂ photovoltaic devices

Guthrey

Nardone

Johnston

et al. 2019

Progress in Photovoltaics

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Partial shading of series‐connected thin‐film photovoltaic modules can force shaded cells into reverse bias, which can cause rapid and irreversible power loss and reduce the practical module lifespan. Unfortunately, this is a common occurrence in field‐deployed modules due to the myriad of environmental factors that can result in partial shading. In this work, we identify as‐grown nonuniformities in the Cu(In,Ga)Se2 (CIGS) absorber layers as the points of origin for the damage induced under reverse‐bias conditions. The structure and chemistry associated with inclusions and voids in the CIGS films cause these features to act as resistive heating elements in reverse‐bias conditions. This localized resistive heating provides the energy required to induce thermal runaway breakdown in the CIGS devices, resulting in damage to charge collection and reduced active area of a device. This mechanism is also described with a robust device model to connect the experimental observations with their physical origins.

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“…Electron-beam- Table 2 and were taken from previous studies. 3,7,8 3 | CHARACTERIZATION OF REVERSE-BIAS BREAKDOWN SITES…”

Section: Methodsmentioning

confidence: 99%

Section: Thermal Runawaymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Characterization and modeling of reverse‐bias breakdown in Cu(In,Ga)Se₂ photovoltaic devices

Guthrey

Nardone

Johnston

et al. 2019

Progress in Photovoltaics

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show abstract

“…Hence, the recommended measurement window of MPP data can be up several days (eg, 3 days in Figure 4), as long as there exists sufficient variation in illumination and temperature to reconstruct the MPP IV. In the case of catastrophic degradation (such as partial shading degradation in thin-film solar modules 32 ), the extracted circuit parameters become the average of pre-degradation and post-degradation values over time. Because the short-circuit current may fluctuate significantly because of soiling and precipitation, the upper and lower bounds thereof are set to be the datasheet short-circuit current and zero, respectively.…”

Section: Step 3: Preprocessing Mpp Datamentioning

confidence: 99%

“…The underlying physical degradation mechanisms of PV are strongly contingent on local meteorological factors and different technologies, eg, solar modules exposed in humid regions are more susceptible to contact corrosion, 46 and monolithic thin-film solar modules are vulnerable to partial shading degradation. 32 Similarly, modules more likely to suffer from PID should adopt Corning ® Willow ™ Glass to impede ion migration. 47 Therefore, ideally, module design ought to be geography and technology dependent.…”

Section: Geography-specific and Technology-specific Reliability-awamentioning

confidence: 99%

Real‐time monitoring and diagnosis of photovoltaic system degradation only using maximum power point—the Suns‐Vmp method

Sun

Chavali

Alam

2018

Progress in Photovoltaics

Self Cite

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The uncertainties associated with technology‐specific and geography‐specific degradation rates make it difficult to calculate the levelized cost of energy, and thus the economic viability of solar energy. In this regard, millions of fielded photovoltaic modules may serve as a global testbed, where we can interpret the routinely collected time series maximum power point (MPP) data to assess the time‐dependent “health” of solar modules. The existing characterization methods, however, cannot effectively mine/decode these datasets to identify various degradation pathways. In this paper, we propose a new methodology called the Suns‐Vmp method, which offers a simple yet powerful approach to monitoring and diagnosing time‐dependent degradation of solar modules by using the MPP data. The algorithm reconstructs “IV” curves by using the natural illumination‐dependent and temperature‐dependent daily MPP characteristics as constraints to fit physics‐based circuit models. These synthetic IV characteristics are then used to determine the time‐dependent evolution of circuit parameters (eg, series resistance), which in turn allows one to deduce the dominant degradation modes (eg, solder bond failure) of solar modules. The proposed method has been applied to a test facility at the National Renewable Energy Laboratory. Our analysis indicates that the solar modules degraded at a rate of ~0.7%/year because of discoloration and weakened solder bonds. These conclusions are validated by independent outdoor IV measurements and on‐site imaging characterization. Integrated with physics‐based degradation models or machine learning algorithms, the method can also serve to predict the lifetime of photovoltaic systems.

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Machine Learning for Perovskites' Reap-Rest-Recovery Cycle

Howard

Tennyson

Neves

et al. 2019

Joule

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Perovskite photovoltaics are efficient and inexpensive, yet their performance is dynamic. In this Perspective, we examine the effects of H 2 O, O 2 , bias, temperature, and illumination on device performance and recovery. First, we discuss pivotal experiments that evaluate perovskites' ability to go through a reaprest-recovery (3R) cycle, and how machine learning (ML) can help identify the optimum values for each operating parameter. Second, we analyze perovskite dynamics and degradation, emphasizing the research challenges surrounding this 3R cycle. We then outline experiments that could identify the impact of environmental factors on recovery for different perovskite compositions. Finally, we propose an ML paradigm for maximizing long-term performance and predicting device performance recovery, including a shared-knowledge repository. By reframing perovskites' optoelectronic transiency within the context of recovery rather than degradation, we highlight a set of research opportunities and the artificial intelligence solutions needed for the commercial adoption of these promising solar cell materials.Joule 3, 325-337, February 20, 2019 ª 2018 Elsevier Inc. 325

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Thermal and electrical effects of partial shade in monolithic thin-film photovoltaic modules

Cited by 13 publications

References 8 publications

Characterization and modeling of reverse‐bias breakdown in Cu(In,Ga)Se₂ photovoltaic devices

Characterization and modeling of reverse‐bias breakdown in Cu(In,Ga)Se₂ photovoltaic devices

Real‐time monitoring and diagnosis of photovoltaic system degradation only using maximum power point—the Suns‐Vmp method

Machine Learning for Perovskites' Reap-Rest-Recovery Cycle

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Thermal and electrical effects of partial shade in monolithic thin-film photovoltaic modules

Cited by 13 publications

References 8 publications

Characterization and modeling of reverse‐bias breakdown in Cu(In,Ga)Se2 photovoltaic devices

Characterization and modeling of reverse‐bias breakdown in Cu(In,Ga)Se2 photovoltaic devices

Real‐time monitoring and diagnosis of photovoltaic system degradation only using maximum power point—the Suns‐Vmp method

Machine Learning for Perovskites' Reap-Rest-Recovery Cycle

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Product

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Characterization and modeling of reverse‐bias breakdown in Cu(In,Ga)Se₂ photovoltaic devices

Characterization and modeling of reverse‐bias breakdown in Cu(In,Ga)Se₂ photovoltaic devices