Photovoltaic EL Fault Detection with SNR-Optimized Imaging
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Solution Overview
Problem
Conventional electroluminescent (EL) detection methods for photovoltaic modules face challenges in capturing high-quality images due to environmental factors like temperature and wind speed, leading to low signal-to-noise ratios, which complicates fault identification.
Innovation Solution
A fault detection method and apparatus that maximize the signal-to-noise ratio of images captured from photovoltaic modules by adjusting power supply parameters, ensuring optimal image quality for efficient fault identification, even in adverse environmental conditions, using an inverter to transmit electric energy and adjust power supply parameters based on preset or reference curves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional EL detection method is used to capture images of photovoltaic modules, then fault detection can be performed, but the signal-to-noise ratio of captured images is low due to environmental factors such as temperature, wind speed, and rain
Solution Approach 1:
The patent changes the power supply parameters (current, voltage, or power) to multiple different values or ranges, capturing images at each parameter setting. By varying these electrical parameters, the system can optimize the electroluminescent effect under different environmental conditions, thereby improving the signal-to-noise ratio of captured images despite adverse environmental factors.
2Measurement precision
If multiple different values or ranges of power supply parameters are used to capture images, then optimal image quality can be achieved, but the detection time and complexity increase
Solution Approach 1:
The patent pre-divides the power supply parameters into multiple different values or ranges before detection. This preliminary structuring of parameter spaces allows the system to systematically and efficiently test different parameter combinations without random trial-and-error, reducing the overall detection time while still achieving optimal image quality through comprehensive parameter coverage.
Solution Approach 2:
The patent employs periodic action by capturing images at different power supply parameter settings in a structured sequence. The system periodically switches between different current, voltage, or power levels, capturing images at each period. This periodic parameter variation ensures thorough testing of different conditions while maintaining a systematic timeline that prevents excessive detection time.
3Measurement precision
If manual confirmation is required to determine optimal signal-to-noise ratio, then image quality can be verified, but the operation complexity and time consumption increase
Solution Approach 1:
The patent implements feedback by automatically determining whether the signal-to-noise ratio of captured images is maximized through image processing and analysis algorithms. The system provides feedback on the quality of captured images and automatically adjusts or selects the optimal power supply parameters based on this feedback, eliminating the need for manual confirmation and significantly reducing operation complexity while maintaining verification of optimal signal-to-noise ratio.
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
Ensures high-quality image capture and simplifies the fault identification process by maintaining optimal signal-to-noise ratios, improving detection efficiency and reducing manual intervention, while allowing for different fault types to be analyzed with specific power supply parameter settings.
Implementation Method 1
the photovoltaic module emits light under action of power supply equipment or an excitation light source
Data Source
AI summary
A fault detection apparatus includes an image capture module and a detection module, and the fault detection apparatus is configured to capturing an image of the photovoltaic module in a light emitting state, and performing fault detection on the photovoltaic module based on the image when a signal-to-noise ratio of the image is maximized, to identify a fault type of the photovoltaic module.