A neural network-based estimation of the level of contamination on high-voltage porcelain and glass insulators

Maraaba, Luqman S.; Al-Hamouz, Z.; Al-Duwaish, H.

doi:10.1007/s00202-017-0634-z

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…More and more scholars have carried out research on image blur removal technology based on the GAN network [11]. Related scholars use theGAN network for SR problems, reconstructing high-resolution images from a single lowresolution image, which can save high-resolution images well [12]. Frequency texture details, and the method can also be applied to image deblurring detail information restoration.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

Insulator Hydrophobic Image Edge Detection Algorithm considering Deconvolution and Deblurring Algorithm

Wang¹,

Ma²

2022

Mathematical Problems in Engineering

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“…The most commonly used neural network for classification purposes is the multi-layer feed-forward neural network (MFNN) [21]. The feed forward NN has the ability to learn various types of complex linear and nonlinear functions.…”

Section: The Proposed Diagnostic Tool Designmentioning

confidence: 99%

An Accurate Tool for Detecting Stator Inter-Turn Fault in LSPMSM

2019

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This paper proposes an accurate diagnosing tool that can predict the stator inter-turn size in line start permanent magnetic synchronous motor (LSPMSM). The proposed diagnosing approach is developed based on an experimentally validated mathematical model of the motor under inter-turn fault. The developed model has been tested using MATLAB R under different loading and fault size conditions. Since the stator currents and voltages are easily accessible, it is decided to use them as the key signatures for developing the diagnostic tool. Several time and frequency-based features have been extracted using motor current and voltage waveforms under different loading and fault size conditions. The developed tool has been designed to correlate the extracted features with its corresponding size of stator inter-turn fault. Finally, testing of the developed diagnosis tool shows a high accuracy of 96% in detecting the size. Moreover, the proposed diagnostic tool is examined against motor parameter variations. The results confirm the robustness of the proposed approach where the accuracy is slightly affected under a wide range of motor parameter variations.

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“…In insulator contamination assessment research, researchers commonly collect data through experiments and apply machine learning methods to evaluate insulators' surface contamination status. In [6], a neural network algorithm was developed, facilitating the evaluation of insulator contamination status through image processing; the instance analysis demonstrated an accuracy rate approaching 85%. In [7], a study estimated insulator surface salt density by measuring surface leakage current and conducted predictive research on insulator contamination using neural networks.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Pollution Characteristics of Ceramic Double-Shed Insulators Based on Dimensionless Analysis

Lv,

Chen,

Wang

et al. 2023

Preprint

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Evaluating the accumulation of contamination on insulator surfaces plays a pivotal role in mitigating the occurrence of pollution flashover incidents. This paper takes the XWP2-160 porcelain double-shed insulator as the subject of study, and utilizes the COMSOL software to perform numerical simulations of the contamination accumulation characteristics. It investigates the influence of voltage, particle density, and spatial pollution concentration on contamination accumulation. Additionally, drawing upon the principles of dimensionless analysis, seven dimensionless parameters are established to comprehensively assess the contamination accumulation characteristics, and using the 1stOpt software, a functional expression representing the relationship between environmental variables and insulator contamination accumulation was obtained by fitting. The results reveal that: 1) with the voltage, particle density, and pollution concentration increase, the insulator's surface contamination level shows an increasing trend; 2) insulator contamination can be effectively characterized and predicted using seven dimensionless criteria, namely Lv, Re, Fr, NEHD, Cl, Cρ, and Cn; 3) the contamination criteria correlation, fitted using power functions, achieves a coefficient of determination (R²) of 0.98, with an average error of 4.1% between the prediction and validation data sets.

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A neural network-based estimation of the level of contamination on high-voltage porcelain and glass insulators

Cited by 10 publications

References 26 publications

Insulator Hydrophobic Image Edge Detection Algorithm considering Deconvolution and Deblurring Algorithm

Insulator Hydrophobic Image Edge Detection Algorithm considering Deconvolution and Deblurring Algorithm

An Accurate Tool for Detecting Stator Inter-Turn Fault in LSPMSM

Prediction of Pollution Characteristics of Ceramic Double-Shed Insulators Based on Dimensionless Analysis

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