Comparison of Drone-based IR-imaging with Module Resolved Monitoring Power Data

Quality control and efficient operation of roof-top residential photovoltaic (PV)-installations is an economic and technical challenge because these PV-systems often suffer from various roof orientations and inclinations as well as partial and temporal shading. Module optimizers, which track the operating point individually for each module group, are good solutions providing also additional electrical information. The purpose of this work is verifying the identification and localization of defective PV-modules with infrared (IR)-imaging and with module optimizers as well as quantifying the impact on the performance. Several PV-plants were inspected and carefully analyzed. The findings using IR-imaging can clearly be classified to substring failures due to open circuit and short circuit, bypass-diode faults, cell defect, and some undefined optimizer problems. The electric data reveal that these findings are related to certain power loss and monthly or annual yield reduction. Interesting is the temporal behavior of particular defective sites, which show short-term recovery. In summary, there is a good agreement between the occasionally performed IR-findings and the continuously monitored electrical data.

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“…The good, defect‐free modules have temperatures of about 23°C. A substring failure leads to temperature increase of 3 to 5 K …”

Section: Resultsmentioning

confidence: 99%

Verifying defective PV‐modules by IR‐imaging and controlling with module optimizers

Buerhop‐Lutz

Fecher

Pickel

et al. 2018

Progress in Photovoltaics

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“…An advantage of correlating the module power and temperature as presented here is that it can be determined by utilizing a module in open circuit as it establishes the lowest point of the correlation. Presented here is an empirical approach which still remains to be validated in the field although some experience could already be gathered with promising results . The approach delivers an easy‐to‐use tool for gathering data on power losses in modules in the field by using IR imaging which can also be applied to large PV utilities by using drone‐mounted IR cameras which is explained further in the following.…”

Section: Interpretation and Discussionmentioning

confidence: 99%

“…In order to outline the application for practical field measurements, a possible maintenance routine is explained in the following given the aforementioned assumptions. The module under test can be identified through IR measurements by observing temperature irregularities as qualitative assessment via IR is already possible as shown in the literature . In a similar fashion, the reference module should be chosen by exhibiting a homogenous temperature distribution.…”

Section: Interpretation and Discussionmentioning

confidence: 99%

“…The module under test can be identified through IR measurements by observing temperature irregularities as qualitative assessment via IR is already possible as shown in the literature. [3][4][5][6] In a similar fashion, the reference module should be chosen by exhibiting a homogenous temperature distribution. As the convective environment can cause a temperature gradient especially on roof-mounted PV installations (leading for example to cooler modules on the roof edges), the reference module should be chosen in close proximity to the module under test.…”

Section: Application For Practical Field Measurementsmentioning

confidence: 99%

“…In the literature, a qualitative correlation between temperature anomalies and the presence of defects is already proven. [3][4][5][6] Although there are some approaches that correlate temperature differences with power loss, [7][8][9] quantitative information regarding the module power is not given, and thus a comprehensive, quantitative, and easy-to-use approach still remains to be found. For use in large-scale PV plants, practicability and fast quantitative assessment are important.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative assessment of the power loss of silicon PV modules by IR thermography and its dependence on data‐filtering criteria

Teubner

Buerhop‐Lutz

Pickel

et al. 2019

Progress in Photovoltaics

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Reliability and quality control of photovoltaic (PV) plants increases in importance as the relevance of PV for the worldwide renewable energy production grows. In this study, a new method is presented which allows for a quantitative assessment of silicon PV module performance solely by relying on the cell temperatures measured via thermography (IR). A module temperature and power key figure are formulated and found to correlate very well with a linear relationship. The dependence of the deduced correlation's precision on measurement conditions is calculated and discussed. It facilitates decision‐making because optimal measurement conditions usually occur only very rarely, such that a compromise between data quality and measurement frequency has to be found. The power loss correlation presented in this paper may be used as part of a maintenance routine in order to ensure the best possible long‐term performance of the PV plant over its lifetime. The practical application in the field is outlined and explained.

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Image based surface damage detection of renewable energy installations using a unified deep learning approach

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Comparison of Drone-based IR-imaging with Module Resolved Monitoring Power Data

Cited by 21 publications

References 4 publications

Verifying defective PV‐modules by IR‐imaging and controlling with module optimizers

Verifying defective PV‐modules by IR‐imaging and controlling with module optimizers

Quantitative assessment of the power loss of silicon PV modules by IR thermography and its dependence on data‐filtering criteria

Image based surface damage detection of renewable energy installations using a unified deep learning approach

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