Naive Bayesian classifier for hydrophobicity classification of overhead polymeric insulators using binary image features with ambient light compensation

Jayabal, Rajamohan; Karuppiyan, Vijayarekha; Sidharthan, Rakesh Kumar

doi:10.1049/hve.2019.0052

Cited by 15 publications

(10 citation statements)

References 24 publications

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“…It is a challenging task to acquire a certain number of insulators with HC1–HC7. Some researchers proposed that using different percentage of isopropyl alcohol by volume as spraying solution could artificially simulate different HCs [12–17]. Isopropyl alcohol has a lower surface tension than water, spray it on the insulator surface, the droplets formed consistent with weak hydrophobic.…”

Section: Spray Images Collectionmentioning

confidence: 99%

“…After that, features were extracted by binary large object (BLOB) analysis. Finally, these features were input into multi‐classifier, such as support vector machine (SVM) [11], BP neural network [9] and naive Bayesian classifier [12], to predict the HCs. These methods require to define statistical and geometrical image features associated with HC [13], which are able to describe the distribution of water droplets on surface of composite insulators.…”

Section: Introductionmentioning

confidence: 99%

“…These methods require to define statistical and geometrical image features associated with HC [13], which are able to describe the distribution of water droplets on surface of composite insulators. The commonly‐used image features include the shape factor [14], the coverage rate of water and that of the maximum droplet [12], and so on. However, it is difficult to effectively extract these features from spray images with various ambient light, shooting angle and surface conditions [15].…”

Section: Introductionmentioning

confidence: 99%

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Hydrophobicity Classification of Composite Insulators Based on Light‐Weight Convolutional Neural Networks

Qiu

Liu

Liao

et al. 2022

IEEJ Transactions Elec Engng

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To achieve accurate and rapid measurement of the hydrophobicity class (HC) of composite insulators, an intelligent spray image recognition technique based on light‐weight convolutional neural networks (CNN) is proposed in this paper. A spray image data set contains clean, contaminated and aged insulators with various illuminations, shooting angles and distances, about 10 400 images of shed surface were collected by spray tests and data augmentation. Five classification models were established by different CNNs, including GoogLeNet, ResNet101, ShuffleNet 0.5×, ShuffleNet 0.25× and MobileNet V2, while the first four of them were pre‐trained by ImageNet dataset. These models were trained, validated and tested by spray image data set. Six indexes were designed to evaluate each model and the discriminative regions for classification were visualized by gradient weighted class activation mapping (Grad‐CAM) method. The results show that these models can effectively recognize spray images with HC1–HC7 and the light‐weight ShuffleNet 0.5× has the best performance, with the classification accuracy of 97.09% for 2022 test images. The Grad‐CAM visualizations indicate that the results have high reliability. This study can provide reference for on‐line detection and intelligent identification of hydrophobicity levels of composite insulators. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Section: Spray Images Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hydrophobicity Classification of Composite Insulators Based on Light‐Weight Convolutional Neural Networks

Qiu

Liu

Liao

et al. 2022

IEEJ Transactions Elec Engng

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“…Due to the imbalance between the current power demand, the scale of the synchronized grid, and the energy consumption structure, in order to meet the needs of largescale optimization of grid configuration and energy consumption under the conditions of the new era and to improve the safe carrying capacity of the grid, it is urgent to strengthen the cross-regional characteristics [5]. In addition to the increase in the number of insulators, the construction of large-scale UHV transmission lines is bound to put forward higher requirements on the insulation characteristics and mechanical strength of insulators, and the insulation characteristics of insulators are closely related to the safe and stable operation of the power grid [6].…”

Section: Introductionmentioning

confidence: 99%

Insulator Hydrophobic Image Edge Detection Algorithm considering Deconvolution and Deblurring Algorithm

Wang¹,

Ma²

2022

Mathematical Problems in Engineering

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In this paper, the Gram matrix is used to calculate the correlation of the filter response sets under different scale kernels learned by each layer of the network in the deconvolution, and the loss between the corresponding feature response correlations in the multilayer network is calculated. Linear summation is used to obtain a stable, multiscale image model representation. This paper extracts the contours of the salient areas of the image and adjusts the parameters of the deconvolution network to learn the salient area patterns of the image. At the same time, for the image to be generated, a shape template is used to limit the range of the area to be generated in order to obtain a shape image with similar patterns. When the spatial relative relationship characteristics of the image constituent objects are obvious, we appropriately add high-level semantic feature activation values for reinforcement. This paper solves the estimation of the unknown blur kernel by using image prior knowledge, filtering and gradient domain algorithms and other different technologies to obtain image jitter or scene movement information and estimate the size, location, and density of the blur kernel. This paper studies a relatively robust deconvolution model, which is insensitive to random noise, has stable effects, and can overcome the water ripple effect caused by the usual convolution process. This paper attempts to study the fuzzy model with variable space. The usual blur is a spatial invariant model; that is, a single kernel is used to describe the motion of all pixels on the image. By selecting different characteristic parameters, this paper conducts experimental research on some existing hydrophobic indicator function methods and calculates the relationship between characteristic parameters and hydrophobicity when different hydrophobic indicator functions are adopted. One characteristic of the hydrophobic image of composite insulators is low contrast. The traditional method of converting color images to grayscale images cannot improve the image contrast. This paper analyzes the hydrophobic image of the composite insulator, and the extracted B channel component image of the hydrophobic image improves the contrast of the image and facilitates the subsequent segmentation of water traces and background. In this paper, the water repellent image's watermark area is counted, and connected-domain wave processing is used to limit the area of water droplets retained, thereby improving the efficiency of filtering water droplets without having a big impact on the image as a whole. The problem of uneven illumination is an unavoidable problem in the field of image processing, and the resulting reflection problem brings difficulties to image processing. This article regards the reflective area of the watermark as a “hole” and uses the idea of “hole filling” to eliminate the reflective point, which weakens the reflection problem to a certain extent.

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“…Subsequently, a voltage stress concentration area is formed around the dry bands, which will break down and cause dry band arcing. The local arcs are further enlarged with the increasing leakage current, which may cause flashover [7, 8].…”

Section: Introductionmentioning

confidence: 99%

Classification of partial discharge severities of ceramic insulators based on texture analysis of UV pulses

Jin

et al. 2021

High Voltage

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Inspection of partial discharge before contamination flashover is of great importance for preventing exterior insulation accidents. In this study, a new method for identification of discharge severities is proposed. Specifically, a low-cost ultraviolet (UV) sensor detection system was combined with time-frequency method, texture analysis and support vector machine (SVM) classifier to classify partial discharge severities for ceramic insulators. The visible images and the root-mean-square value of leakage currents detected simultaneously are used to classify the UV signals into different discharge faults. The frequency and amplitude integration of UV pulses are minimum in corona discharge and larger in arc discharge. The images of UV signal spectrograms differ significantly at different discharge stages. The density and brightness of image textures are minimal in corona discharge and larger in arc discharge. Valid and reliable features selected by two texture feature extraction methods with SVM classifier have a reliable classification accuracy of 90.6% for ceramic insulators, and outperform a single time feature or other texture features. SVM outperforms k-Nearest Neighbour, Naive Bayes and Decision Tree. Our new method with computational effectiveness and high practicality can solve the problem of high randomness and low accuracy of UV sensor detection. It can be further applied to the deterioration diagnosis of power facilities.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Naive Bayesian classifier for hydrophobicity classification of overhead polymeric insulators using binary image features with ambient light compensation

Cited by 15 publications

References 24 publications

Hydrophobicity Classification of Composite Insulators Based on Light‐Weight Convolutional Neural Networks

Hydrophobicity Classification of Composite Insulators Based on Light‐Weight Convolutional Neural Networks

Insulator Hydrophobic Image Edge Detection Algorithm considering Deconvolution and Deblurring Algorithm

Classification of partial discharge severities of ceramic insulators based on texture analysis of UV pulses

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