Learnings from Thermography of over 12 GWs of Solar PV Plants

2 min read

Solar PV plants consist of hundreds of thousands of discrete components exposed to various stresses throughout the asset lifecycle. Many of these stresses can lead to defects that manifest as hotspots and impact the plant generation. Timely identification and resolution of these issues reduce generation losses and safeguard equipment. Drone-based infrared thermography coupled with Machine Learning analytics improves detection accuracy, and reduces the time to detect and rectify hotspots.

From drone thermography studies of over 12GW of solar PV plants (over 30 million modules) across five continents, we have learned that: 

1: The median prevalence of hotspots was 1.4% of the total installed modules
The median prevalence of hotspots was 1.4% of the installed modules, while the average incidence was 2.1%.

2: Best plants had <0.1% hotspot prevalence compared to 30% for the plants with worst scores
In the utility-scale solar PV plants we surveyed, 0.05% to 30% of the total modules installed were affected by hotspots.

3: Hotspot induced energy loss estimates range from 0.5% – 3% of total generation
Meeting PR warranty does not necessarily imply optimum generation from your plant. Theoretical modeling of losses indicates that addressing hotspot issues can help improve energy yield between 0.5-3% of total generation.

4: Median hotspot temperatures found to be 10-15°C on an STC1 adjusted basis
The typical median temperature gradient of hotspots was 4-7°C on a nominal basis and 10-15°C on an STC adjusted basis.

5: Single-cell hotspots typically have the highest temperature gradients
Single-cell hotspots tend to have the highest temperature gradients. These hotspots are typically caused by shadowing due to vegetation, bird droppings, a thick dust layer, or malfunctioning cells with microcracks or defects.

6: Rooftop solar fares poorly compared to utility-scale plants
Rooftop solar plants are worse off compared to utility-scale solar plants. The median prevalence of hotspots was 3.6% compared to 1.4% for utility-scale plants.

7: There is no correlation between the size of the plant and hotspot prevalence
We did not find a correlation between the plant’s size and the prevalence of hotspots. There are plants >100 MW in size with less than 0.1% capacity impacted by hotspots.

8: Drone thermography has a quick payback ranging from 3-6 months
Depending on project size, yield improvement, and PPA rate, a thermal scan’s payback tends to be in the range of 3-6 months.


Given the ease of thermal data collection and analysis with drones, we recommend the following:

  1. Thermal drones should become an integral part of the solar PV plant O&M toolkit.  
  2. At a minimum, a once-a-year 100% thermal scan is desirable.
  3. More frequent scans with in-house drone scan capability are likely to give maximum benefit for large solar plants.
  4. Surveys should be conducted after panel cleaning to avoid noise from dirt-related hotspots.
  5. Use software integrated with robust AI/ML-based analytics to enable rapid diagnosis and rectification of hotspots. 
  6. The use of software with ticketing and workflow functionality enables rapid identification and rectification of hotspots on site. 

Are you conducting or planning to conduct thermography for your solar PV plants?
Please contact us at sales@sensehawk.com to discuss your requirements for thermography that helps you improve plant performance and health.

1Standard Test Conditions: normalized to 1000 W/m2 irradiance

SenseHawk

SenseHawk

SenseHawk, Inc. is a provider of cloud based drone data analytics and productivity tools for the solar industry. With customers in 15 countries, SenseHawk solutions are used by solar & EPC companies for terrain analytics, construction monitoring, thermography and task management on solar sites

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