Why has my thermographic inspection data been rejected?

Why has my thermographic inspection data been rejected?


Above provides clear data guidelines to ensure the highest quality of analysis of each solar plant. Please see the issues below to provide context as to why the data could be rejected.

Corrupt files

  1. When there is a considerable number of frames that have corrupted, causing areas to be missed. 
  2. On occasion a file will not open, other times there will be issues with how the imagery is recorded, like the example below. 

Cropped/Missed footage

  1. There are a few reasons why this may happen but the main two are caused by the drone path and the drone height. 
  2. If the drone has not used terrain following on a hilly area it will cause the drone to fly too close to the tables at times, this means there will be parts of modules never captured. Below is an image you see on the drones first pass and the second is the returning pass. 

  1.  The drone following the incorrect path can result in missed areas, this can be due to flight planning or weather conditions.

 

Low quality footage

Data that is not like our normal standard can be expected when not flown in the right conditions. Low irradiance, temperature and too early in the day are a few examples and will cause data to look mostly like the below. 


RGB reflection

When the sun is reflecting from the panels as the drone fly’s overhead, you may need to adjust the angle of the camera as it can sometimes cause the RGB to be overexposed. 


Incorrect GSD

Data flown consistently higher than the spec of the survey in most zones. This can vary slightly and some grace is given as it is not always easy to achieve, below is an example of the GSD variation we usually encounter. 



Non consistent height

If flown over hilly terrain and there is a large variation in heights caused by not using terrain following then this will mean the survey will not always be the correct GSD. 


Drone heading non fixed

The drone must maintain a fixed heading every pass that it does, below is an example of how this would look.

Drone flying perpendicular to tables

If the drone is not flown parallel to the tables this will cause automation to fail and be very time consuming to analyse, example of the flight path the drone might take in this case. 

Flight log format

Flight must be in xlsx format using one of our compatible templates, all of the values need to be there including things like wind speed, cloud cover like the below.

A close-up of a tableAI-generated content may be incorrect.

 

Inter-row reflection/shading 

More commonly an issue in the later parts of the season, there are two ways they can be an issue. 
 
Inter-row reflection is where the reflection from the next row is causing a heated strip on the modules as seen below. 
​


Inter-row shading is where the next row is blocking the light and causing a dark area, more common when flown too early/late in the day or too late into the season.





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