Fig.5 shows a DPL image of a large section of the solar farm, containing approximately 1,200 PV modules in one single image. Only modules located in the bottom half of this image are connected to the inverter that was switched, i.e. only half of the modules yield a PL image, the other half appearing dark in the difference image, as expected. Further, the image includes a grassy area to the right of the PV array, which also appears dark. The image was calculated from 20 individual near-infrared camera images. We observe an average PL signal count in the section containing the switched modules of 290 counts, whereas the count rate in both the grassy area and the non-switched section is below 10 counts. Four modules in the image stand out in red. They exhibit considerably lower PL counts compared to all other modules in that section of the solar farm. The PL intensity from these modules is approximately 53% compared to the surrounding modules.
Quantitative analysis of luminescence intensities from solar cells is based on the Generalised Planck Equation , predicting an exponential relationship between the emitted luminescence intensity and the diode voltage of the device. In simplified form that relationship can be written as
\(I_{\text{PL}}=Ce^{\left(\frac{\text{qV}}{\text{kT}}\right)\ }\)(1)
where IPL is the emitted luminescence intensity,k is Boltzmann’s constant, q is the elementary charge,T is the sample temperature and V is the diode voltage. The so-called thermal voltage kT/q has a value of about 25.85 mV at room temperature. The calibration constant C in Eq.1 depends on various sample specific geometric and optical sample parameters, on the spectral sensitivity of the detection system and on various measurement parameters. The accuracy of obtaining implied voltages from luminescence signals is therefore linked to the ability to accurately determine the constant C . By contrast, it follows from Eq.1 that the ratio of two luminescence signals, IPL,1 andIPL,2 respectively, can be converted into a voltage difference ΔV via
\(V=\frac{\text{kT}}{q}\ln\frac{I_{PL,1}}{I_{PL,2}}\). (2)
provided that the calibration constant C can be assumed to be the same for the two luminescence measurements. That is the case if measurements are conducted with the same measurement system (as is the case here) and with identical measurement parameters (as is also the case here) and either on the same sample at different times or under different bias conditions or on different samples which can be assumed to have similar optical properties.
Eq. 2 predicts 16.5 mV lower voltage per cell for the four modules that appear in Fig.5 with 53% lower PL counts. During our experiments we noted some variation in optical properties between modules in this solar farm in the near infrared spectral range that is defined by the bandpass filter mounted on the camera lens. A detailed quantitative analysis of DPL results obtained from this solar farm, including the expected minor impact of these optical variations is currently underway and will be reported elsewhere, noting however that the focus of this publication is the demonstration of the inverter-based DPL image acquisition principle. We would like to emphasise, that the quantitative analysis of DPL image variations between cells or modules of the same type in terms of voltage variations according to Eq.2 is well established. For validation purposes we performed separate experiments on modules at a UNSW outdoor test system. These measurements confirmed that the accuracy of implied voltage differences between modules from PL images is on the order of +/- 2 mV per cell when PL image data is compared to measured terminal voltages under well controlled experimental conditions.
The DPL image example in Fig.5 demonstrates, for the first time, the ability to simultaneously acquire PL images of very large numbers of PV modules, in similar fashion to what is currently common practice with RPA-based thermal infrared imaging. RPA-based camera deployment enables not only the acquisition of images of large sections of the farm from a high vantage point, but also convenient and rapid change of the camera field of view by variation of the distance to the target. For the experiments discussed above positioning of the RPA prior to image acquisition was achieved with manual RPA flight control. For the near future we envisage automated mapping of solar farms with pre-programmed flight paths and way points, as is current practice for thermal inspection. In this scenario we envisage the use of high-level overview PL imaging data captured from RPAs, as shown in Fig.5, in combination with either close-up RPA-based imaging or high-fidelity imaging from the ground. This will enable rapid overview images to be acquired first, followed by more detailed inspection of specific modules or farm sections with suspected defects.

Discussion

The ability to acquire outdoor DPL images of entire PV systems or of large sections of solar farms using inverter-based modulation is a very significant step towards cost effective and commercially viable daylight luminescence imaging of large utility scale solar systems. The analysis of relative PL intensities is a particularly powerful quantitative tool to assess voltage variations between and within modules, and to quantify module degradation. Of particular importance in this context is the robustness of PL image data against variations in sample temperature. Zafirovska et al.  showed that the temperature coefficient of implied voltages that are derived from luminescence is about ten times lower than the temperature coefficient of the terminal voltage. This is particularly relevant here since measuring temperatures on PV modules in the field with good accuracy is generally difficult to achieve. While such accurate temperature measurements are a key requirement for the analysis of terminal voltage measurements of fielded modules, it is not required for quantitative PL image analysis due to the low temperature sensitivity of the PL signal.
The inverter-based manipulation of the operating point of large numbers of modules that are connected to specific inverters in combination with RPA-based image acquisition is an elegant solution to inspect large solar assets, and to find faulty modules or determine module degradation. No modifications to the system wiring are required, it can be performed during the day and does not require large and heavy equipment such as power supplies and generators to be brought on site. Daylight PL imaging also avoids additional hazards (electrical, night-work) and administrative difficulties associated with working at nighttime.

Summary and Conclusions

High quality daylight photoluminescence image acquisition of crystalline silicon solar modules in both rooftop and utility-scale solar farms using electrical modulation of the operating point of modules connected to an inverter was demonstrated. Measurements were performed by manipulating the operating point of modules using existing functionality of commercial inverters, without specific customisation of either hardware or firmware, without any modifications to the PV system’s electrical wiring and without utilising additional large power electronic equipment, such as generators and power supplies. Importantly, measurements were performed in daylight during regular working hours, avoiding the administrative and practical complications and risks associated with nighttime operation.
It was shown that close-up high-resolution and high-quality images of individual modules enable the detection of microcracks and individual cell level defects. In contrast, RPA based overview images of large sections of farms, with up to 1,200 modules contained in a single PL image, as demonstrated here, allow the detection of coarse cell, module, and system-level defects. Quantitative analysis enables accurate assessment of voltage variations within and between modules. The work presented in this paper is a significant step towards commercially viable, fast and effective daylight photoluminescence imaging becoming a routine part of large-scale PV plant inspection.