Solar Panel Defect Detection Using Thermographic Orthomosaics
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Solution Overview
Problem
Existing performance monitoring systems for solar power plants lack accuracy in detecting and quantifying power losses, particularly from defects in solar panels that do not generate alarms, leading to underperformance and energy loss.
Innovation Solution
An automated system utilizing aerial vehicles to capture visual and thermographic images of solar panels, which are then processed to create orthomosaic images and signatures. These signatures are used to identify defects and calculate energy losses in each string of solar panels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional performance monitoring systems compare measured power with predicted values at inverter level, then some sense of quantum of losses can be provided, but accurate detection and diagnosis of specific defect sources cannot be achieved
Solution Approach 1:
The system segments the solar power plant into individual solar panel-level analysis units. Instead of monitoring at inverter level only, the system divides the plant into multiple strings and individual panels, capturing images and electrical data at each segment to identify specific defective panels and quantify their individual energy losses.
Solution Approach 2:
The system introduces an intermediary aerial vehicle (drone) equipped with imaging sensors that flies between the ground and solar panels to capture visual and thermographic images. This intermediary enables access to panel-level data without requiring ground-level intervention or complex installation on each panel.
2Productivity
If solar power plants operate at large scale with huge number of solar panels, then energy generation capacity increases, but fault detection and maintenance difficulty increases
Solution Approach 1:
The system enables self-service monitoring where the solar panels essentially monitor themselves through captured images and electrical data. Defective panels reveal their own conditions through visual signatures (cracks, soiling) and thermographic signatures (hotspots), eliminating the need for manual inspection of each panel in large-scale plants.
Solution Approach 2:
The system changes the monitoring parameters from electrical measurements only to include visual parameters (image analysis) and thermal parameters (thermographic analysis). This multi-parameter approach enables detection of defects that do not generate electrical alarms, such as soiling losses and minor panel degradation.
3Measurement precision
If automated performance monitoring is implemented at panel level with image capture and processing, then accurate defect detection and energy loss quantification can be achieved, but system complexity and processing requirements increase
Solution Approach 1:
The aerial vehicle serves multiple functions: capturing visual images, capturing thermographic images, navigating to target panels, and transmitting data. The single platform performs what would otherwise require multiple separate systems, reducing overall system complexity despite the advanced capabilities required.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides accurate and automated performance monitoring, enabling the detection of defective solar panels and quantification of energy losses, thereby improving maintenance efficiency and reducing energy wastage.
Implementation Method 1
at least one aerial vehicle to capture visual images and thermographic images of the at least one solar panel
Data Source
AI summary
There is disclosed a system for performance monitoring of at least one solar panel of a solar power plant, comprising at least one aerial vehicle communicably coupled with a data-processing arrangement, wherein the data processing arrangement is configured to receive visual images and thermographic images of the at least one solar panel; stitch the visual images and the thermographic images to create an visual orthomosaic image and a thermographic orthomosaic image respectively; create visual and radiometric signatures solar panels using the visual orthomosaic image and the thermographic orthomosaic image respectively; create at least one table in the thermographic orthomosaic image; create a table-to-string mapping; identify at least one defect in the solar panels based on the visual signatures and the radiometric signatures; calculate energy loss in each of the at least one string in the solar power plant.