Introduction to Aerial Inspections: Page 3 of 3

Best Practices

For optimal visibility into plant performance, operators can perform annual aerial inspections that cover 100% of the modules for a given site, then supplement these data with targeted module-level I-V tracing. Technicians should regularly capture supplemental I-V curve traces for a constant subset of modules that represent all of the major serial number batches deployed on-site. For best aerial inspection results, operators should pay careful attention to both data collection and post-survey data processing.

Data collection. Relatively steady high-irradiance conditions are required for the flyover component of an aerial inspection. As is the case with commissioning or performance tests, the minimum irradiance for an aerial inspection is 600 W/m2. Ideally, the irradiance should not vary by more than 100 W/m2 during the survey, as steady-state conditions allow for a better comparison of results across the site.

An aerial survey should collect both IR and visible imagery. The quality of these data must be adequate to allow for fault detection. Data quality depends on both image resolution and sensitivity. Image resolution is a function of the size of each pixel, with smaller pixels resulting in a more detailed image. Sensitivity, meanwhile, is a function of a camera’s ability to distinguish between small variations in temperature (IR camera) or light (conventional camera).

To detect major module faults during an aerial site survey, an IR inspection system needs to have a resolution of at least 19 cm/pixel. It is possible to identify defects on a subcell level, after post-survey data processing, if the IR inspection system has a resolution of at least 15 cm/pixel. To detect minor temperature fluctuations between modules, the IR camera should have a noise equivalent temperature difference (NETD) rating of no more than 20 mK.

For visible measurements, image resolution is the key metric. I recommend using an imaging system with a minimum resolution of 3 cm/pixel. With this level of resolution, it is possible to identify small amounts of surface soiling, such as vegetation or bird droppings, which can cause localized hot spots. The identification of hot spots due to actual cell damage requires data processing to compare IR and thermal imagery, then filter out those hot spots associated with soiling or other external causes.

Data processing. This is the most critical part of an aerial inspection. The data processing methodology must be able to not only detect faults and distinguish between fault modes, but also accurately locate the faults within the array. It is not enough to know that faults exist. For technicians to remediate problems efficiently, aerial survey results must locate faults within the array down to the module level.

Since the processing software traditionally used for non-PV site surveys is not compatible with IR imagery from PV systems, the technician or analyst needs to either process these data manually or process them using custom, proprietary software. Manual data processing requires that someone scroll through the recorded video feeds looking for and locating faults. As a result, this technique is prone to error and may require a follow-up visit to verify results in the field. By comparison, operators can use validated automated techniques directly for field remediation, warranty prosecution and system planning.

Rob Andrews / Heliolytics / Toronto, ON /

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