All Collections
Operations & Maintenance
Use Cases
Using Regions to compare panel brands
Using Regions to compare panel brands

Learn how to use the regions feature to analyse which panel brand / model exhibits the most problems.

Updated over a week ago


This guide shows you how to use the regions feature to indicate where specific panel brands and models are used throughout the site. These regions can then be used to analyse the problems that occur on each brand/model.

Define panel type regions

The first step is to draw/import the regions for the different panel brands/models.

You can learn more about the regions feature here.

Create a new region type called Panel Brand & Model to highlight where each brand/model is used.

Draw the regions indicating where each brand/model is used.

Drawing of the regions indicating where each brand/model is used.

If the same model is used on disjunct regions of the sites, you have to give them the same name. Results from these regions will be bundled in Statistics mode.

The region types work hierarchically, meaning that whatever region is on top will be the prominent region across the platform.

When enabling the regions layer, it will be used for the statistics mode, the default regions, and the site overview. This is important for the next step.

Analyse inspection results

Now that you've drawn these regions, you'll notice the following benefits when opening your latest inspection:

  • Every anomaly now contains a Panel Brand and Model property.

  • The PDF and CSV exports highlight the panel brand & model of each anomaly.

  • Charts view splits the statistics per panel brand & model in the detail charts (click on the charts on the left to show them). You'll notice that the statistics in the PDF export also show these charts.

It's the final benefit we'll use here to analyse the results. To learn more about the Charts mode and how to access it, you can use this support article.

If you've filled in your site properties correctly, you'll immediately notice a loss breakdown table highlighting the loss for each panel brand and model.

Screenshot of Statistics Mode

The other charts can refine your analysis. By looking at the causes chart, you can see that one of the models suffers more from shadowing, which skews the results.

Going to Filters on the top and selecting only the Physical internal cause highlights what's going wrong inside the panels.

Screenshot of Statistics Mode

If you look at the anomalies chart, you'll notice that Brand A Model 1 suffers more from Potential PID issues than the other models, and Brand A, in general, seems to have more problems.

The anomaly statistics here aren't normalised on each region's total number of modules. The statistics can be downloaded using the button on the top right for a more detailed analysis.

Did this answer your question?