1. Better understand the impact anomalies found during thermal inspections have on the performance of your solar site
   
2. Focus on solving issues on the lowest performing assets
   
3. Understand which areas seem to be suffering more from anomalies found at a string and module level
   
4. Target your activities on site more efficiently and intelligently
   
5. Make a judgment as to the viability of repairing issues
   
6. Compare this estimation with your string monitoring data
   

What makes our Power Loss Estimation better than others?

1. The Power Loss Model has been built by CREST (Centre for Renewable Energy Systems) at the University of Loughborough, which is one of the most renown renewable energy research institutions in Europe.
   
2. The model takes into account the impact each anomaly has on production but more importantly is able to understand the impact of having multiple anomalies in a given electrical area.
   
3. The Power Loss Estimationn model enables our clients to have an accurate power loss estimation for every key area of their site (string, inverter, transformer), and therefore allow them to target their time on-site intelligently.
   
4. The Power Loss Estimation will be used to compare and calibrate string monitoring data - with the aim to integrate the data to improve our clients' user experience.
   

Testing our Power Loss Estimation model

1. The CREST model was given the module properties (incl. number of cells), modules per string, number of strings per inverter and their layout and all the anomalies from the thermographic inspection.
   
2. The standard model uses the same anomalies, where a simple, theoretical performance reduction value is applied to each module anomaly and aggregated, regardless of the fact that this is not how the electrical circuit behaves.
   
3. Overall Results:
   
1. CREST model = 2.09% losses
   
2. Standard model = 2.38% losses
   
4. There is quite a substantial change at the string level

1. See the overall impact, at the time of the inspection
   
2. Have a visual indication of the performance of each inverter (colour-coded)
   
3. Get an exact % loss of each inverter zone
   
4. Understand the difference between Sum of Losses & Total Operating Loss - the most important part of the model
   
5. See that we can estimate the potential annual revenue loss
   
6. Get a Summary of the losses per anomaly type

Anomaly Summary

1. Average Loss - We calculate the loss across the whole system if each anomaly was the only one. This is the average of those.
   
2. Average Replacement Gain - This calculates, on average for each type, how much power would be restored by resolving an individual anomaly within the system.
   
3. Total Replacement Value - The replacement gain is calculated by simulating the site with 1 specific defect removed, if you continued to remove defects until there was only 1 left you would see a gain of the average loss value instead (See definition of average loss).

Inverter Power Losses

1. Get a visual and numerical indication of the losses (Total Operating Loss)
   
2. Sort by:
   
1. alphabetical order
   
2. # of anomalies
   
3. % loss
   
3. Hide the "Optimal" strings ( < 1% MPP loss)
   
4. Click on a string to view what are the issues

String Power Losses

1. Resolution Gain - this reflects the overall impact of this anomaly. The figure includes losses incurred elsewhere in the system. It gives an indication of the effect of resolving this individual anomaly
   
2. Operating Loss - this measures the losses of the module. The loss is due to this anomaly but also the effects from other anomalies elsewhere in the system.
   
3. Maximum Power Point (MPP) Loss - this shows how the capacity of this module is lowered by this specific anomaly

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