Design of early fault detection technique for electrical assets using infrared thermograms

Jaffery, Zainul Abdin; Dubey, Ashwani Kumar

doi:10.1016/j.ijepes.2014.06.049

Cited by 65 publications

(12 citation statements)

References 40 publications

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“…Considering the similarity of the structure among the three phases of the termination, the reference regions can be positioned according to the abnormal heating area's position information and the size ratio of different phases of terminations identified in the image. Taking Figure 13 as an example, (10) and (11) were used to calculate the coordinate information of the reference regions' pixels. The highlighted areas in Figure 13…”

Section: Cable Terminationsmentioning

confidence: 99%

“…In previously published researches, the image features related to the temperature gradients of equipment were used as the input of neural networks for the autonomous diagnosis of electrical equipment. In order to analyze the temperature-related information, Rahmani et al used the Zernike moment as an image feature of fuse bases [7]; Huda et al extracted the first-order histogram and gray level co-occurrence matrix of infrared images captured from main switchboards [8][9][10]; Jaffery et al extracted the RGB color moment of images of fuse cabinets [11]. In the above studies, the key features needed to be selected manually, which was, in fact, a heuristic process.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories

Mu¹,

Xü

Xia³

et al. 2021

Energies

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Infrared thermography has been used as a key means for the identification of overheating defects in power cable accessories. At present, analysis of thermal imaging pictures relies on human visual inspections, which is time-consuming and laborious and requires engineering expertise. In order to realize intelligent, autonomous recognition of infrared images taken from electrical equipment, previous studies reported preliminary work in preprocessing of infrared images and in the extraction of key feature parameters, which were then used to train neural networks. However, the key features required manual selection, and previous reports showed no practical implementations. In this contribution, an autonomous diagnosis method, which is based on the Faster RCNN network and the Mean-Shift algorithm, is proposed. Firstly, the Faster RCNN network is trained to implement the autonomous identification and positioning of the objects to be diagnosed in the infrared images. Then, the Mean-Shift algorithm is used for image segmentation to extract the area of overheating. Next, the parameters determining the temperature of the overheating parts of cable accessories are calculated, based on which the diagnosis are then made by following the relevant cable condition assessment criteria. Case studies are carried out in the paper, and results show that the cable accessories and their overheating regions can be located and assessed at different camera angles and under various background conditions via the autonomous processing and diagnosis methods proposed in the paper.

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Section: Cable Terminationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories

Mu¹,

Xü

Xia³

et al. 2021

Energies

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“…1 shows the block diagram of thermal image analysis with image segmentation [12]. Image segmentation of thermal images makes use of digital image processing to achieve desired segmented object [13]. The 3D temperature plot for a thermal image can also be obtained for better results [14].…”

Section: Thermal Image Analysis With Image Segmentationmentioning

confidence: 99%

Thermal Image Processing for High Temperature Regions with Image Segmentation and 3D Temperature Plot in Solar Battery

Chaudhary¹

2018

IJIGSP

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Solar batteries are the essential component in an off-grid solar photovoltaic generation system and used for the purpose of storage. Normally lead acid batteries are used for solar applications and are placed in a battery room where the temperature must be maintained with in safe working limits. The temperature of battery depends on several factors like ambient temperature, load current drawn by the battery, sulphur deposition terminals, charging and discharging cycles. When temperature of a battery increases beyond safe limit problems of heating arises. Heating reduces the battery life and may be one of the reasons of explosion in batteries. The ambient temperature measuring device used in battery room is not sufficient to measure the heat generated by batteries. Installation of a normal camera in battery room is capable to monitor the smoke or spark in battery components but not the amount of temperature so it is also not fulfilling the need. Thermal imaging camera captures heat coming out from a battery and produces a thermal image with temperatures associated with it. This thermal image is used for representing high and low temperatures with a range of maximum and minimum values of temperature. In this paper a heated battery is identified in a battery room for solar photovoltaic generation system and thermal image analysis is performed to determine the regions of high temperature with the help of image segmentation and 3D temperature plot. The image segmentation is done using marker based watershed transform technique to achieve the heated region of interest from thermal image and 3D temperature plot shows the area of maximum temperature with location in thermal image of heated solar battery.

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“…Although such an approach requires the starting points to be well projected, and the components must be positioned in the center of the thermal image to be detected. The authors of [21] use a color-built segmentation technique to extract hot points from the thermal images. They use the color segmentation approach applied to a specific portion of the IR image and modeled on the temperature distribution pattern.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Image-Based Defect Detection in High Voltage Electrical Equipment

et al. 2020

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The increase in the internal temperature of high voltage electrical instruments is due to a variety of factors, particularly, contact problems; environmental factors; unbalanced loads; and cracks in the high voltage current transformers, voltage transformers, insulators, or terminal junctions. This increase in the internal temperature can cause unusual disturbances and damage to high voltage electrical equipment. Therefore, early prevention measures of thermal anomalies in equipment are necessary to prevent high voltage equipment failure that might shut down the whole grid system. In this article, we propose a novel non-destructive approach to defect analysis in high voltage equipment by taking advantage of the infrared thermography and the deep learning (DL) approach from the machine learning paradigm. The infrared images of the components were captured using the FLIR T630 without disturbing the operations of the power grid. In the first stage, rich features maps from the convolutional layers of the AlexNet pretrained model were extracted. After feature extraction, the random forest (RF) and support vector machines (SVM) were trained for learning of the defective and non-defective high voltage electrical equipment. In an experimental analysis, the RF optimally learned the separation between defective and non-defective equipment with greater than 96% accuracy, outperforming all the other comparative approaches for deep and nondeep features. The proposed approach based on the RF is reliable and shows its efficacy for fault detection in high voltage electrical equipment.

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Design of early fault detection technique for electrical assets using infrared thermograms

Cited by 65 publications

References 40 publications

Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories

Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories

Thermal Image Processing for High Temperature Regions with Image Segmentation and 3D Temperature Plot in Solar Battery

Deep Learning Image-Based Defect Detection in High Voltage Electrical Equipment

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