Drone start-up finds new way to inspect solar farms

A South African drone services start-up says it has found a new way to inspect, monitor and maintain a solar power plant.

Cape Town-based Integrated Aerial Systems (IAS) specialises in bespoke drone data collection and processing solutions for various industries, which include agriculture and solar plant inspections.

While providing inspection services to Concentrated Solar Power Plants – a renewable energy technology services provider that uses mirrors or lenses to focus sunlight onto a small area to generate heat, which is then used to drive a turbine and generate electricity – IAS says it developed a new survey method, which uses drone technology instead of manual inspections.

“This allowed our client to fast-track warranty claims on certain components, carry out targeted repairs, and get accurate and actionable data to monitor the maintenance and performance of the CSP plant,” the company said in its report.

Below is the IAS report on how they did it. We hope you enjoy.

When it comes to aerial surveys of solar farms, drones are commonly used to detect anomalies on photovoltaic panels. When sunlight is absorbed by a module, it is converted into electricity.

However, if cells on the module are not optimally converting the irradiance from sunlight into electricity, they heat up and produce ‘hot spots’. These hot spots can be picked up by the drone’s thermal camera, which is highly sensitive.

Visual (RGB) vs Thermal (White hot pallet) imagery. Picture: IAS

A typical Solar PV site inspection involves a drone with a thermal and visual camera. The drone flies in a grid pattern collecting thousands of images, which are then processed using machine learning systems to accurately identify and categorise anomalies across the site.

These inspections have become standard practice, with specific data capturing methodologies dictating the type of reporting outputs.

However, Concentrated Solar Power (CSP) Plants’ solar energy generation methods are different though. The company use mirrors to concentrate thermal irradiance to generate electricity. These mirrors reflect and concentrate sunlight onto a specific point (in this case, a Heat collecting element (HCE)) to create heat.

This heat is then stored in a medium, such as molten salt, and then run through a heat exchanger, which in turn creates steam. This steam drives a turbine, which creates electricity. The plant runs in a ‘closed loop’ system, which basically means that water and molten salt are recycled.

Automatically detected high-temperature tubes with poor insulation. Picture: IAS

Inspecting this kind of site required a methodology to be developed from the ground up, as no industry-established methodology existed.

This lack of guidelines meant that Integrated Aerial Systems (IAS) developed its own methodology.

However, several complications were encountered during this process, including the reflective nature of the mirrors, the distance between the strings, and the oscillating movement of the Parabolic trough.

Due to these complications, flights had to be done manually, which was far slower than an automated inspection. In total, approximately 38,000 images were produced from the inspection using the DJI Matrice210 V2 drone.

Processing the data was the next challenge. As a bespoke solution like this had not been conducted before, IAS had to develop a customised processing solution through a Geospatial software house.

The goal was to identify partially- and fully-dislocated internal shields (which are responsible for creating a vacuum inside the parabolic tube), as well as broken tubes.

A machine learning model was developed and trained to analyse each image and identify these shields and broken tubes. However, the model had difficulty quantifying the data.

To solve this, a separate algorithm was run to provide a minimum and maximum number of possible broken elements, as an exact number could only be determined by visually inspecting each image.

While identifying dislocated shields can only be done with visual inspection and high-resolution cameras, broken elements and those that have vacuum loss are easily identifiable with thermal imagery coupled with the RGB data using temperature delta.

A loss of vacuum in the HCE does not allow heat to dissipate, and a significant deviation in temperature is observed. By applying a second RGB layer, elements that are broken and those that are not can be separated.

The results of the survey were exceptionally accurate and saved the company significant time and money over the alternative of a manual inspection.

They were able to use IAS’s data as proof for warranty claims on certain components as well as fast-track targeted repairs and maintenance.


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