Electroluminescence Imaging for Microcrack Detection in Solar Cells

A fault point locating system for photovoltaic power generation systems that enables precise location of faults in photovoltaic modules beyond traditional arc fault detection. The system measures the current flowing through any battery string and local cable connections, enabling the detection of faults between modules, cables, and the ground. This approach addresses the limitations of traditional arc fault detection methods by capturing fault conditions through current measurements rather than voltage characteristics, and provides a comprehensive monitoring solution for photovoltaic power generation systems.

Source

High-precision photovoltaic module EL detection method and system for accurate monitoring of photovoltaic modules. The method employs voltage excitation of the photovoltaic component, followed by photodetector-based voltage measurement. The system employs precise control of the excitation voltage to optimize detection performance, particularly for modules operating near their maximum power point. This approach enables the detection of subtle defects such as micro-cracks and localized heating through advanced signal processing and analysis.

Source

Solar photovoltaic power generation component fault detection system that enables real-time monitoring of cracks and hot spots in solar panels through automated, remote detection. The system comprises a photovoltaic panel frame assembly with a counterweight, position adjustment mechanism, EL detector, infrared thermal imager, and a photovoltaic power generation component fault detection system. The system's position adjustment mechanism enables precise positioning of the EL detector and infrared thermal imager within the protective enclosure, while the photovoltaic power generation component fault detection system monitors panel performance and detects faults through advanced thermal imaging.

Source

A solar panel defect detection system that leverages deep learning-based image analysis to enhance existing inspection methods. The system employs a high-resolution camera to capture detailed images of solar panels, which are then processed by computer algorithms to detect defects that conventional low-resolution cameras cannot identify. The system integrates a light irradiation system to optimize panel exposure and improve defect detection accuracy. This approach enables the detection of microscopic defects that conventional inspection methods cannot capture, while maintaining the cost-effectiveness of existing inspection methods.

Source

Photovoltaic fault detection using multispectral fusion through computer vision. The method combines infrared and visible spectral images of photovoltaic arrays to enhance feature extraction and detection accuracy. The infrared image captures detailed thermal signatures, while the visible image provides structural information. The fused images are then processed using machine learning techniques to identify photovoltaic faults through advanced feature extraction and analysis. This multispectral fusion approach enables improved detection capabilities compared to traditional infrared-only methods, particularly in environments with varying environmental conditions.

Source

An electroluminescence detection system for photovoltaic modules that prevents damage during detection. The system comprises a mounting frame, a plug-in piece, and a power-on piece. The plug-in piece is inserted between the photovoltaic module and the lead-out wire, with the power-on piece abutting the lead-out wire. This configuration ensures the photovoltaic module remains intact during the detection process, as the plug-in piece separates the module from the lead-out wire. The power-on piece maintains contact with the lead-out wire during detection, preventing damage to the module's surface.

Source

Low-cost, high-accuracy system for detecting abnormalities in solar cells like decreased power generation and hot spots. The system periodically acquires the operating voltage of a solar cell string. If the time-series variation in voltage exceeds a threshold, it indicates an abnormality in the cell string. This allows detecting cell issues without additional environmental sensors.

Source

Photovoltaic cell defect detection using polarization imaging of infrared radiation. The method employs a polarization-sensitive imaging system that converts infrared radiation emitted by photovoltaic cells into polarized radiation. The polarization characteristics of the infrared radiation are analyzed through a multi-polarization imaging process, which produces a detailed 3D representation of the cell's internal structure. This enables precise defect detection by analyzing the unique polarization patterns that occur when defects occur in the cell.

Source

Photovoltaic cell fault detection using a modulated light matrix approach. The method involves generating modulated light signals at different frequencies to each photovoltaic cell, then superimposing these signals to form a total short-circuit current. By analyzing the amplitude of the generated photocurrents, the method can determine the presence and location of faults in individual cells, including cracking, contact resistance issues, and internal resistance variations. This approach enables precise fault location and characterization through the unique spectral signatures of each cell.

Source

Detecting faults in photovoltaic systems using IV curves and advanced signal processing techniques. The method combines IV curve analysis with machine learning algorithms to identify and quantify faults in photovoltaic panels, strings, and fields. By leveraging IV curves and advanced signal processing, the method can accurately diagnose faults such as shading, dirt accumulation, short circuits, and bypass diode failures, enabling proactive maintenance and reducing system downtime.

Source

A photovoltaic cell surface defect detection method that enhances defect detection accuracy through multi-color illumination. The method involves capturing images of photovoltaic cells under three distinct color illumination conditions (e.g., red, green, and blue) in a dark environment. These images are then combined to produce a single color image, which is analyzed to identify defects. The multi-color illumination approach improves defect detection by providing a more accurate representation of the photovoltaic cell's surface characteristics compared to monochromatic illumination.

Source

Solar cell module earth leakage detection system for PV systems to prevent output degradation during fires. The system monitors input and output currents from individual solar cells, compares them to reference thresholds, and automatically separates modules with deviations from normal operation. This enables the system to detect and isolate short circuits that can cause electrical fires, while maintaining overall system efficiency.

Source

Fault detection for photovoltaic modules using image analysis. The method maximizes signal-to-noise ratio (SNR) of captured images from photovoltaic modules to enable accurate fault detection. The system employs an image capture device to capture images of the photovoltaic module during operation, and a detection apparatus analyzes these images to identify module faults. The detection process continuously monitors SNR levels and adjusts the image acquisition parameters when necessary to maximize SNR before sending the image to the fault detection system.

Source

Device for enhancing defect detection in solar cells through improved electrical injection efficiency. The device comprises multiple probe rows, each comprising independent segments connected to a common fixed resistor with a resistance range of 0.1-100 ohms. The fixed resistor is positioned at a distance from the probe segments, allowing the segments to operate independently while maintaining consistent resistance values. This configuration enables the device to accurately measure carrier recombination light generated by defects in the solar cell, thereby improving defect detection accuracy compared to traditional external current injection-based methods.

Source

A method for improving the detection of solar cell defects through enhanced electroluminescence analysis. The method employs segmented probe rows with fixed resistive values between 0.1-100Ω, with the far end connected to the power supply. Each row of segmented probes is connected to the main grid, while the far end of the fixed resistor is connected to the solar cell. This configuration enables the detection of internal defects by analyzing the current distribution across the solar cell when the external current is zero.

Source

Improving power-on uniformity in photovoltaic cell EL detection through a novel method. The method involves connecting the two ends of each welding strip to a fixed resistance resistor, then applying DC power to the strips. This configuration eliminates the conventional soldering contact issues by mechanically connecting the ends of the strips to a fixed point, ensuring consistent resistance values across the string. This approach eliminates the variability associated with soldering and flux buildup, resulting in more uniform EL imaging across the cell string.

Source

A camera system for diagnosing solar cell defects through direct imaging of electric field emission. The system employs a high-pass filter to selectively transmit wavelengths above 700 nm, allowing detailed imaging of the solar cell's internal structure while suppressing visible light. The system features a low-pass filter to remove visible light, enabling precise imaging of the electric field emission patterns. The camera system can capture both static and dynamic images of the solar cell, enabling rapid defect detection and characterization.

Source

Power supply device for solar cell inspection using EL imaging, which eliminates the need for a diesel generator. The device converts the solar cell module's DC output into a direct current (DC) using a power conditioner, and then supplies the DC current to an imaging device for EL imaging. The imaging device captures EL light emission from the solar cell module, which is then used to generate images for quality inspection. The power conditioner ensures reliable DC output from the solar cell module, eliminating the need for an external power source.

Source

Real-time solar cell defect detection system for manufacturing processes that combines traditional image processing and deep learning techniques. The system employs a combination of automated defect detection methods, including image segmentation, filtering, and classification, to automatically identify defects in solar cells during production. The system achieves high accuracy in detecting common solar cell defects like black chips, welds, and broken cells, while also detecting more complex defects like splinter defects. The detection process is enabled by a convolutional neural network (CNN) that can handle various defect types, and the system operates at speeds comparable to human detection.

Source

A system for detecting and analyzing anomalies in solar cell performance through a multi-spectral monitoring approach. The system captures images of the solar panel array using visible light and thermal infrared sensors, then analyzes the data to identify potential issues. The system uses advanced algorithms to combine visible and infrared data, and employs machine learning to detect anomalies based on both spectral characteristics and temporal patterns. The system provides detailed information on the detected anomalies, including their location, nature, and potential impact on overall system performance.

Source