Early hotspot detection in photovoltaic modules using color image descriptors: An infrared thermography study

Infrared thermography, in the analysis of photovoltaic (PV) power plants, is a mature technical discipline. In the event of a hailstorm that leaves the PV system without the support of the power grid (and a significant portion of the generation potential), thermography is the easiest way to determine the condition of the modules and revive the existing system with the available resources. This paper presents research conducted on a 30 kW part of a 420 kW PV power plant, and demonstrates the procedure for inspecting visually correct modules that have suffered from a major natural disaster. The severity of the disaster is shown by the fact that only 14% of the PV modules at the test site remained intact. Following the recommendations of the standard IEC TS 62446-3, a thermographic analysis was performed. The thermographic analysis was preceded by an analysis of the I-V curve, which was presented in detail using two characteristic modules as examples. I-V curve measurements are necessary to relate the measured values of the radiation and the measured contact temperature of the module to the thermal patterns. The analysis concluded that soiled modules must be cleaned, regardless of the degree of soiling. The test results clearly indicated defective module elements that would result in a safety violation if reused. The research shows that the validity criterion defined on the basis of the analysis of the reference module can be supplemented, but can also be replaced by a statistical analysis of several modules. The comparison between the thermographic analysis and the visual inspection clearly confirmed thermography as a complementary method for testing PV-s.

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Infrared Thermography Based Hotspot Detection Of Photovoltaic Module using YOLO

2021 IEEE 12th Energy Conversion Congress & Exposition - Asia (ECCE-Asia) ◽

10.1109/ecce-asia49820.2021.9478998 ◽

2021 ◽

Author(s):

Tahmid Tajwar ◽

Shara Fatema Hossain ◽

Ovib Hassan Mobin ◽

Mohaimenul Islam ◽

Fariha Reza Khan ◽

...

Keyword(s):

Infrared Thermography ◽

Photovoltaic Module ◽

Hotspot Detection

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Quaternion fractional-order color orthogonal moment-based image representation and recognition

EURASIP Journal on Image and Video Processing ◽

10.1186/s13640-021-00553-7 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Bing He ◽

Jun Liu ◽

Tengfei Yang ◽

Bin Xiao ◽

Yanguo Peng

Keyword(s):

Fractional Order ◽

Color Image ◽

Laguerre Polynomials ◽

Color Images ◽

Polar Coordinates ◽

Coordinate Space ◽

Geometric Invariant ◽

Global Features ◽

Orthogonal Moments ◽

Image Descriptors

AbstractInspired by quaternion algebra and the idea of fractional-order transformation, we propose a new set of quaternion fractional-order generalized Laguerre orthogonal moments (QFr-GLMs) based on fractional-order generalized Laguerre polynomials. Firstly, the proposed QFr-GLMs are directly constructed in Cartesian coordinate space, avoiding the need for conversion between Cartesian and polar coordinates; therefore, they are better image descriptors than circularly orthogonal moments constructed in polar coordinates. Moreover, unlike the latest Zernike moments based on quaternion and fractional-order transformations, which extract only the global features from color images, our proposed QFr-GLMs can extract both the global and local color features. This paper also derives a new set of invariant color-image descriptors by QFr-GLMs, enabling geometric-invariant pattern recognition in color images. Finally, the performances of our proposed QFr-GLMs and moment invariants were evaluated in simulation experiments of correlated color images. Both theoretical analysis and experimental results demonstrate the value of the proposed QFr-GLMs and their geometric invariants in the representation and recognition of color images.

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Novel Color Orthogonal Moments-Based Image Representation and Recognition

10.21203/rs.3.rs-127190/v1 ◽

2020 ◽

Author(s):

Bing He ◽

Jun Liu ◽

Tengfei Yang ◽

Bin Xiao ◽

Yanguo Peng

Keyword(s):

Fractional Order ◽

Color Image ◽

Laguerre Polynomials ◽

Color Images ◽

Polar Coordinates ◽

Coordinate Space ◽

Geometric Invariant ◽

Global Features ◽

Orthogonal Moments ◽

Image Descriptors

Abstract Inspired by quaternion algebra and the idea of fractional-order transformation, we propose a new set of quaternion fractional-order generalized Laguerre orthogonal moments (QFr-GLMs) based on fractional-order generalized Laguerre polynomials. Firstly, the proposed QFr-GLMs are directly constructed in Cartesian coordinate space, avoiding the need for conversion between Cartesian and polar coordinates; therefore, they are better image descriptors than circularly orthogonal moments constructed in polar coordinates. Moreover, unlike the latest Zernike moments based on quaternion and fractional-order transformations, which extract only the global features from color images, our proposed QFr-GLMs can extract both the global and local color features. This paper also derives a new set of invariant color-image descriptors by QFr-GLMs, enabling geometric-invariant pattern recognition in color images. Finally, the performances of our proposed QFr-GLMs and moment invariants were evaluated in simulation experiments of correlated color images. Both theoretical analysis and experimental results demonstrate the value of the proposed QFr-GLMs and their geometric invariants in the representation and recognition of color images.

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