Intelligent Thermographic Diagnostic Applied to Surge Arresters: A New Approach

Almeida, Carlos Alexandre Laurentys de; Braga, Antônio de Pádua; Nascimento, Sara Batista do; Paiva, V.; Martins, H.J.A.; Torres, R.E.; Caminhas, Walmir M.

doi:10.1109/tpwrd.2009.2013375

Cited by 92 publications

(21 citation statements)

References 2 publications

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“…Image Acquisition: This is the first step of digital image processing. Image acquisition could be as simple as being given an image that is already in digital form [3].five hundred images of the faulty equipments in different substation were collected having different temperature range for further processing.…”

Section: Methodsmentioning

confidence: 99%

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“Fault Prediction in Electrical Equipments using Thermographic Inspection”

Palimkar¹,

Bhole²

2016

IJERT

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Electrical power system deals with various new technologies, Also most part of electrical system in various industrial applications is automated. To maintain the reliability it is essential to carry out maintenance procedures. Temperature is one of the important parameter that should be taken into account in the maintenance, because higher temperatures indicate loss of energy in the form of heat. As temperature audits have much importance in the maintenance, now a day Thermo-graphic audits are carried out in many industries. Thermography is nothing but temperature profiling of a surface of electrical equipment. One of the major advantages of this type of technique is that this is contactless monitoring of electrical equipment without actually shutting down the equipment which gives reliable and fast prediction of faulty part of the system. In this paper image processing techniques are used for the analysis of thermal images of the faulty equipments. this paper deals with the study and analysis of Thermography as one of the fault prediction technique.

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Section: Methodsmentioning

confidence: 99%

“…Image preprocessing [3] involves extracting ROI (Region of interest) as we are working mainly on the faulty part of the equipment, converting image to grayscale image, image resizing and image enhancement. Image enhancement is nothing but manipulating image so that result is more suitable.…”

Section: B Image Preprocessingmentioning

confidence: 99%

“Fault Prediction in Electrical Equipments using Thermographic Inspection”

Palimkar¹,

Bhole²

2016

IJERT

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“…C.A.L. Almeida et al (2009) investigated the potentials of neuro‐fuzzy network in classifying the thermal condition of power equipment by material, rated voltage, manufacturer, pollution index, distance, emissivity, ambient temperature and relative humidity [71]. A probability neural network model with temperature eigenvector as characteristic parameters was constructed to diagnosis the low or zero resistance faults and pollution faults of the insulator string [72].…”

Section: Machine‐assisted Fault Diagnosismentioning

confidence: 99%

Infrared thermography‐based diagnostics on power equipment: State‐of‐the‐art

et al. 2020

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As a non‐contact temperature distribution measurement method, infrared thermography (IRT) has emerged as an indispensable tool in condition monitoring and fault diagnosis of electrical equipment based on the absolute and relative temperature values. Manual fault inspection, as an expert‐experiences based evaluation method, has formed a mature technical scheme with a large number of application cases. However, the efficiency and accuracy of manual fault inspection are being challenged by the rapid growth in the number of equipment in power grid. The situation is improving with the advanced of image processing technique. Machine‐assisted fault diagnosis provides a novel method to assist human beings to complete fault diagnosis under the intervention of human prior knowledge. However, the limitations of infrared images bring challenges to image analysis processing especially target detection. In pursuit of automatic fault diagnosis, deep learning algorithms are introduced to achieve target detection in the complex environment. This study reviews the development of IRT‐based diagnostics beginning with the general procedures, objects, and limitations of IRT‐based fault inspection, and then gives an insight into the popular machine‐assisted fault diagnosis as well as image‐based intelligent fault identification. In addition, the future recommendations of IRT are also provided from construction of intelligent infrared detection system, establishment of an open and shared infrared image database and comprehensive utilization of joint visualization diagnosis technology.

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“…The authors of [19] propose a technique only for insulators that fuses the features of infrared and ultraviolet image segmentation based on the Otsu and PSO-BPNN approach. The research presented in [20] utilizes a neuro-fuzzy method for the recognition of defects in arresters, while the inputs of the artificial neural network (ANN) are infrared images and specific identified features. The watershed technique is sensitive to noise in images and non-uniform to colors in complex scenes.…”

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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Intelligent Thermographic Diagnostic Applied to Surge Arresters: A New Approach

Cited by 92 publications

References 2 publications

“Fault Prediction in Electrical Equipments using Thermographic Inspection”

“Fault Prediction in Electrical Equipments using Thermographic Inspection”

Infrared thermography‐based diagnostics on power equipment: State‐of‐the‐art

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

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