Submitted Successfully!
Thank you for your contribution! You can also upload a video entry related to this topic through the link below: https://encyclopedia.pub/user/video_add?id=34449
Check Note
2000/2000
Ver. Summary Created by Modification Content Size Created at Operation
1 -- 6468 2022-11-14 09:20:52 |
2 format correct + 1 word(s) 6469 2022-11-15 03:50:02 |
Major High-Voltage Insulator Contamination Level Classification Techniques
Edit
Upload a video

Insulators are considered one of the most significant parts of power systems which can affect the overall performance of high-voltage (HV) transmission lines and substations. High-voltage insulators are critical for the successful operation of HV overhead transmission lines, and a failure in any insulator due to contamination can lead to flashover voltage, which will cause a power outage. The electrical performance of HV insulators is highly environment sensitive. The main cause of these flashovers in the industrial, agricultural, desert, and coastal areas, is the insulator contamination caused by unfavorable climatic conditions such as dew, fog, or rain. The different methods adopted to identify the contamination level on high-voltage insulators are discussed.

high-voltage insulators neural network support vector machine
Information
View Times: 44
Revisions: 2 times (View History)
Update Time: 15 Nov 2022
Table of Contents

    1. Introduction

    Insulators are regarded as a highly significant power system component that can influence the general performance of the substations and transmission lines. For example, medical and steel mill electrical loads require an uninterruptible power supply. Therefore, any degradation in the insulator performance may result in a sizeable loss of service and revenue. There are two types of high-voltage (HV) insulators based on the material used—ceramic and nonceramic insulators—and based on the place of installation in overhead transmission lines—pin, suspension, strain, and shackle insulators.
    The electrical performance of HV insulators is highly environment sensitive. The withstand and flashover voltages of an insulator are reduced when it is polluted and wetted [1][2]. The conventional porcelain and glass insulators that have shown durability in field conditions are very heavy items that have quite a significant impact on the mechanical requirements of the HV transmission networks [3][4]. For the heavily contaminated conditions where much longer strings are required to be erected, the impact becomes quite limiting on the design parameters, and therefore, heavy reliance on expensive, tedious, and time-consuming maintenance programs becomes an essential part of the design consideration. Electric utility companies have the very challenging task of providing a reliable electric power supply to highly demanding consumers [5]. Moreover, the reliability requirements are becoming more stringent as the comfort of life becomes more and more power dependent. The electric companies, therefore, welcome any technological advancement in the insulator material which could facilitate their professional understanding with ease without compromising the reliability of the power system. Since the 1960s, composite insulators have been introduced as a potential replacement for conventional porcelain and glass insulators. These insulators offer several advantages in terms of being lightweight, easy to handle, resistant to vandalism, and relatively low cost [6][7][8]. Various materials such as ethylene propylene diene monomer (EPDM), room-temperature-vulcanized (RTV) silicon rubber, and High Temperature Vulcanizing (HTV) silicone rubber were employed to manufacture the polymeric insulators [5][9][10][11].

    2. High-Voltage Insulator Contamination Level Classification Techniques

    2.1. Machine Learning

    Neural network (NN) is a kind of machine learning, and it is also called artificial neural network (ANN), where the network employs complex mathematical models for data processing. NN connects a network of units called neurons, and the collection of these neurons constructs a network called a neural network. In addition to the neurons, NN contains links and weights, activation functions, layers, hyperparameters, learning rate, and cost function. Each ANN in the system consists of a set of layers with multiple neurons worked by using activation functions, and it is designed to be adjusted to a dynamic input. Indeed, each neuron receives different versions of the input along with a weight value, and it is then added to a small value called bias. After that, it is passed to the activation function that determines the final output value.

    2.1.1. Backpropagations Neural Network (BPNN)

    Jin and Zhang [12] proposed a technique to figure out the contamination severity in the ceramic insulator based on the feature fusion of both the ultraviolet (UV) and infrared (IR) image information. After preprocessing the images, a Fisher criterion was applied to gain features of IR and UV images. For feature fusion, kernel principal component analysis (KPCA) was adopted to reduce the dimension of the generated features and obtain only a three-dimensional fused feature. These features were fed into a particle swarm-optimized backpropagation neural network (PSO-BPNN) classifier to realize the contamination grade. Paper [10] presented a developed intelligent technique for specifying the contamination level of high voltage (HV) insulators. Maraaba, Al-Hamouz et al. adopted two methods in the feature extraction stage to extract the main features from the insulator images. The first is matrix manipulation, and the second is edge-based segmentation. After that, the singular value decomposition (SVD) was applied to obtain the linear algebraic features. Then, a multilayer feed-forward neural network was fed with these features to predict the ESDD level of the insulator. He, Luo et al. [13] proposed a learning model based on the radial basis function neural network (RBFNN) and the collected infrared images from the porcelain insulators to define the contamination level. They used the first-order, second-order, and third-order color moments as the main features, along with the relative humidity for the detection process. Since RBFNN has many parameters that affect its accuracy and speed, they used the support vector regression technique to define the number of hidden centers. Another technique based on statistical analysis was used to determine the initially hidden centers. This improved learning model combined with the random number control factor and gradient descent algorithm to achieve higher accuracy with less time. In paper [1], Maraaba, Alhamouz et al. proposed a neural network model for predicting the contamination level of the HV glass and porcelain insulators. It indicates the contamination level without the requirement of the deposition of hydrophobic materials and depends on the captured images. They extracted two types of features based on singular value decomposition (SVD): linear algebraic features and histogram-based statistical features. Then, they constructed three neural network scenarios for testing one type of these features or both, and the output of the neural network was the contamination levels. In a paper [14], Al Khafaf and El-Hag presented a new learning model based on a Bayesian regularized neural network to predict the future values of the leakage current. They used the recorded leakage current signal, and they selected some components from the signal. These components were fed into the neural network to predict the future time series of both the fundamental and third harmonic of the leakage current.
    Patel, Maarouf, et al. [15] proposed a technique of pollution level estimation that can be adopted on live lines with the use of pattern recognition and image processing technology. Template matching and grayscale histograms were used on the collected images to clean and extract the main features. Three classification methods were used (i.e., Quadratic discriminant analysis (QDA), polynomial, artificial neural network (ANN), and Leave-one-out cross-validation (LVOCV)) to predict the pollution level, and all of them achieved high classification accuracies. An intelligent contamination prediction model proposed in [16] is based on an optimized backpropagation (BP) neural network with a genetic algorithm (GA). Here, Jinlei, Chao, et al. used the genetic algorithm to make the learning model faster and more robust for any change. The extracted features that were used to feed the BP are temperature, precipitation, wind, relative humidity, and air quality index (AQI). And the output of the BP model is the equivalent salt deposit density (ESDD) and nonsoluble deposit density (NSDD). In [17], Suhaimi, Bashir et al. studied the UV signals’ time and frequency components of the insulators under different contamination levels by using artificial neural networks (ANNs). The experimental studies showed that there is a high correlation between the discharge intensity levels. Hence, this was used to extract the total harmonic distortion and fundamental frequencies from the signal. Then, the selected features were fed into the ANNs model to determine the flashover prediction with respect to the discharge intensity level of the insulator. In paper [18], Yan, Gang et al. designed a risk monitoring interface based on neural networks and fussy logic technologies for predicting the insulator flashover. It is constructed based on a trained backpropagation network to define the real-time state of the insulator. The input vector of the neural network was leakage current amplitude, relative humidity, and the ratio of the 3rd harmonic of the leakage current to the amplitude of the fundamental harmonic. In addition, the output of the neural network was the security state of the insulator linked to the fuzzy interface. They used three fuzzy subsets to represent the security state of the insulator (i.e., safe state, light alarm, and serious alarm). Liu, Yang, et al. [19] adopted the use of a backpropagation(BP) neural network in developing a local insulator pollution diagnosis device to provide a real-time diagnosis of the pollution in the insulators. This local insulator has the ability to communicate with the ultraviolet imager in real time. They used a set of parameters (i.e., apparent discharge, detection distance and gain, and photoelectron number) obtained from the ultraviolet imager as input of the BP neural network. The output of the BP neural network was the pollution level.

    2.1.2. Support Vector Machine (SVM)

    Zhao, Jiang, et al. [20] presented a new insulator technique for predicting the severity of the contamination level and avoiding any pollution flashover accidents. They used a set of features based on the environmental and experimental variables for finding the contamination level: relative humidity (RH), ambient temperature (T), leakage current (LC), namely the maximum pulse amplitude (Ih), the energy ratio (K), and the energy (E). Then, a variation from these features was obtained and fed to the least squares support vector machine (LS-SVM) model to define the level. Xia, Song, et al. [21] proposed a learning method that combines the S transform and the support vector machine (SVM) for classifying the contamination level of porcelain insulators. They used S transform in the feature extraction stage to extract the phase and the amplitude of each frequency point of the recorded leakage current signal. Hence, three main parameters (i.e., amplitude, phase, and total harmonic distortion (THD)) were selected as the input to the SVM model, and the output of the SVM was fed into four fuzzy subsets to determine the level. In this paper [22], Mahdjoubi, Zegnini, et al. improved the performance of the outdoor insulators by using an intelligent detection method based on the least square support vector machines (LS-SVM) learning strategy. They set the support vectors according to a quadratic Renyi criterion by adopting the training set. Insulator height, leakage length, insulator diameter, number of elements in the string, surface conductivity, and number of sheds were used as the input of the LS-SVM model, and the output was the flashover voltage. Here, the training data for this model were generated based on the finite element method. Abedini-Livari, Eshaghi-Maskouni, et al. in [23] discussed the partial discharge (PD) on polymeric insulators under different changes in the impact of physical defects, accelerated salt–fog aging process, and varying amounts of contamination. Here, they recorded the partial discharge of the insulators by using a UHF antenna. A wavelet packet tree was used in extracting the feature from the partial discharge signal. Then, the selected features (i.e., Skewness, kurtosis) were fed into a support vector machine (SVM) model to predict the condition of the insulator. Chen, Li et al. in [24] proposed a classifier based on a support vector machine and the color characteristic of visible images to predict the contamination level. They used an ellipse segmentation technique based on randomized Hough transform to extract the main feature from the visible images. So, around 36 types of characteristics of HSV and RGB components were extracted from the previous process. The mean and median of the S component were selected as the main features based on the Fisher criterion. These features were then fed into the support vector machine classifier to predict the contamination level. In a paper [25], Liu, Mei et al. presented a machine-learning technique based on a semisupervised support vector and the use of photothermal radiometry (PTR) for predicting the contamination level. PTR was used in the measurement to define the pollution severity parameters: NSDD and ESDD on the transient and frequency thermal radiation characteristics of the contamination. Then, the main features were extracted, and their dimensions were reduced using principal component analysis (PCA). After that, a semisupervised classifier was fed with the remaining features to predict the contamination level based on a four classes problem. In paper [26], Sun, Zhang et al. proposed a learning model that combines the exploratory factor analysis (EFA) and the use of a support vector machine for predicting the contamination level of the insulators. They used EFA to minimize the factor variables, which could reduce the complexity of the model. Then, the selected factor variables were fed into the least squares support vector machine (LSSVM) to predict the contamination level. To achieve better results, they applied a nondominated sorting genetic algorithm II for defining the optimal LSSVM parameters. Results showed that the optimized EFA-LSSVM model outperforms the original LSSVM, multiple linear regression, and backpropagation neural network model in the model performance. Zhou and Chen [27] adopted the support vector machine model with data mining techniques to predict the flashover voltage under different gray and salt densities. The average value of the collected data points should not exceed 10%. The results showed that the support vector machine regression model improved the model performance in terms of error values and prediction accuracy, and it provided a reference for the measures of the insulators.

    2.1.3. The k-Nearest Neighbours (KNN) Algorithm

    Chaou, Mekhaldi, et al. [28] proposed a new method called recurrence quantification analysis (RQA), which has the ability to indicate the Recurrent Plot (RP) structures and to quantify the leakage current dynamics during the process. It was proposed to study the RP structures and leakage current dynamics and extract the main features from the current signals for detection purposes. Hence, eight RQA indicators were used to study and investigate the leakage current signals under different conductivities. After that, the mean values of the eight RQA indicators are considered as the input to KNN in order to predict the contamination severity.
    Abouzeid, El-Hag, et al. [2] developed a nonintrusive method based on a machine learning technology to monitor and evaluate the silicone rubber insulators by predicting ESDD level. They used stepwise regression in the feature extraction stage and PCA to reduce the dimension of the extracted features from the leakage current. KNN was adopted to predict the ESDD level. In [29], Xia, Ren et al. proposed a new learning model based on hyperspectral imaging technology (HSI) for evaluating the high-temperature-vulcanized silicone rubber insulators. The Canny operator method was applied to the collected hyperspectral images to select the interesting areas and extract the spectral data. They also used a multivariate scattering correction (MSC) method to pre-treat the extracted data and PCA to reduce the dimension of the extracted features. Then, a successive projection algorithm (SPA) was applied to define the targeted bands. These bands were fed to KNN to predict the contamination level. Ma, Jin, et al. [30] proposed a new learning technique based on the texture features from the UV signals to predict the samples into local arcs, coronas, and long arcs. The texture analysis technique was adopted into the images obtained from the spectrograms of UV signals, and it was used to figure out the Tamura features and the grey-level co-occurrence matrix (GLCM). Then, the extracted features were fed to KNN to classify the partial discharge fault.
    In [31], Sit, Das et al. presented an efficient method to predict the contamination level of the polymer insulators. They analyzed the leakage current in the time–frequency domain using hyperbolic window Stockwell transform (HST), and they extracted a two-dimensional complex time–frequency HS matrix. Then, they divided the HS matrix into magnitude and phase spectrum, and hence 16 features were extracted from the spectrum. Next, they used the least absolute shrinkage and selection operator (LASSO) method to select the best features (i.e., five features) from the extracted ones. The selected features were fed into KNN to predict the contamination level. In [32], the KNN classifier based on insulator images is used for detecting contamination levels. The 40 porcelain insulators used in this study were artificially polluted. Six statistical features were extracted from insulator images and considered as inputs to the classifier, such as mean, variance, asymmetry, kurtosis, energy, and entropy. The classifier showed 85.17% accuracy using k-fold cross-validation. The accuracy of KNN was compared with other classifiers such as decision tree, ensemble subspace, and support vector machine and outperformed them.

    2.1.4. Random Forests (RF)

    In [33], Kannan, Shivakumar, et al. presented a machine learning technique based on a random forests (RF) classifier for classifying the contamination level of the HV insulators. A set of experiments was conducted, and the leakage current (LC) was recorded in the lab on the porcelain insulator at 11 kV AC voltage. They used the discrete wavelet transform technique and time-domain analysis to extract the histogram and basic features of the leakage current. Around 48 features were extracted from the current and then fed to the RF model in order to define the pollution severity. Ren, Li et al. [34] proposed a new learning technique for predicting the pollution severity of the insulators based on random forests (RFs). Moreover, they proposed 16 factors that are linked to the nonsoluble deposit density (NSDD) and equivalent salt deposit density (ESDD) for the learning process. Then, they adopted the mutual information (MI) theory for the feature extraction process based on the weights of the 16 factors. The regression model of RFs was constructed based on the extracted features and tested to predict the ESDD and NSDD levels and then compared with the result of the support vector machines (SVM) model. In [35], Sit, Chakraborty, et al. proposed a learning method based on the mathematical morphological function and the random forests classifier to classify the contamination level in the porcelain insulators. Leakage current was collected on different contamination levels from extensive experiments. They used different statistical operations and mathematical morphological functions in the feature extraction stage. Then, a different number of features (i.e., 1, 2, 3, and 21) was fed into the random forests classifier to predict the contamination level.

    2.1.5. Ensemble Learning (EL)

    Stefenon, Grebogi, et al. in [36] solved the faults in insulators as a multiclass problem using an ensemble extreme learning machine (EN-ELM) and particle swarm optimization. They applied 13.8 kV (rms) in contaminated, drilled, and good insulators and recorded the data using an ultrasound detector connected to a computer. They used wavelet energy coefficient, bottom-up segmentation, and principal component analysis in the feature extraction stage. The extracted features were fed into the optimized ensemble extreme learning machine to predict the class of contamination in the insulator. In paper [37], Qiu, Wu et al. proposed a detection technique based on the hyperspectral concept and machine learning technology. They collected samples from the hyperspectral images with different pollution levels by using a hyperspectrometer. Then, they used multiplicative scatter correction and black-and-white correction to correct the collected images. After that, they obtained from the corrected images the hyperspectral curves using the region of interest (ROI). The extracted features from these images were fed into a multiclassification model of extreme learning machine (ELM) to detect the pollution degree of the insulator. Stefenon, Ribeiro, et al. [38] proposed a learning model based on stacking ensemble in the prediction of polluted porcelain insulators. They used ultrasound equipment to record the signal and then a wavelet transform to filter the signal and remove the noise effects. The extracted signal was fed into a stacking ensemble model to predict the contamination of the insulator. A set of metrics was introduced in the results as mean absolute percentage error (MAPE), coefficient of determination (R2), and root means square error (RMSE). Yin, Xiao, et al. [39] presented a technique based on spectral characteristics and the hyperspectral image to detect the pollution degree in the insulators. They extracted image texture, characteristic color data, and hyperspectral spectral line characteristics of the insulator using the gray-level gradient co-occurrence matrix (GGCM) and used kernel principal component analysis (KPCA) to reduce the dimension of the extracted features. Then, they fused the selected features to be used in the detection process. Next, the fused features were fed into an ensemble learning model to classify the sample into one of the four levels (i.e., light, medium, heavy, and very heavy).

    2.1.6. Convolutional Neural Network (CNN)

    A convolutional neural network (CNN) model was proposed in [40] for the diagnosis of the state of the porcelain insulators in the transmission lines. Liu, Pei, et al. used infrared image technology and then fed them to the LeNet CNN model; it was applied to optimize the network structure. The model showed a high classification rate, and it is robust and offers a better rate under different conditions such as humidity, temperature, thermal load, and position of deterioration on the insulator. In [41], a deep learning model was developed to find the zero-sequence insulators with different air humidity, contamination, and different locations. The authors used infrared images after removing the noise effect and increasing the contrast in the method. The output images were fed into a regional proposal network (RPN) and fast region-based CNN (RCNN) detection network for detecting the insulators. In [42], Feng, Xuran et al. proposed a deep learning model to locate and identify the defects of the insulators by the use of infrared images. They collected the infrared images and then filtered the interference of the background. Then, the cleaned images were fed into the multitarget detection algorithm YOLO for detecting the defects based on multifeature fusion. Once the defect is located in the infrared image, then the type of the defect is identified accordingly. In [43], Mussina, Irmanova, et al. proposed a fusion convolutional network (FCN) architecture for the evaluation of the contaminated outdoor HV insulators. FCN adopts the multimodal information fusion (MMIF) of UAV images with the leakage current and classifies the contamination of the insulator into conditions that are present before the failure, such as snow, water, salt, and metal dust. Using MMIF in the model reduced the complexity of the learning process and achieved better accuracy. In a paper [44], Waleed, Mukhopadhyay, et al. developed a drone-based system for monitoring the ceramic insulator on the power lines. The drone system is equipped with a Raspberry Pi single-board computer and onboard cameras to monitor the state of the insulators. The system also has the capability to perform some computer vision tasks related to the monitoring process; it can perform these tasks onboard or onshore at a ground station. In the case of onshore mode, the drone takes images and simultaneously transmits them to the ground station. Then, object detection methods (i.e., Single-Shot MultiBox Detector (SSD) Mobilenetv2) can be applied to classify the insulators into three levels: healthy, dirty, and broken insulators, while in the onboard mode, images were fed directly to region-based CNN (RCNN) to predict the level. In a paper [45], Liu, Lai, et al. proposed a convolutional neural network (CNN) model by using the discharge image to predict the pollution state of the insulators. They applied the binarization, grayscale, and main spot on the collected images in the feature extraction stage. Then, the extracted features were trained using a CNN model, and the pollution state was defined. The results showed that the discharge state of the insulators is positively correlated with humidity and surface pollution. In a paper [46], Zhao, Yan et al. proposed a learning model based on the hyperspectral technology for predicting the states in the porcelain insulators. They extracted the edges from the images using Gaussian filtering and the Canny algorithm to locate the cracks. Then spectral information was used to predict the state of the insulator by using the Efficient Net CNN model. The results showed that the achieved accuracy by using this model is 96%, which is better than other learning models and without the use of hyperspectral data. In a paper [47], Vigneshwaran, Maheswari, et al. proposed a learning model based on feature fusion and the dual-input VGG convolution neural network (CNN) for predicting the pollution severity of the insulator. The measured partial discharge signal was shaped as a time–frequency image named by a scalogram and 3D phase-resolved partial discharge (PRPD) patterns. The authors fed the dual-input CNN by the scalogram and 3D PRPD patterns. Then, a weighting fusion method was used to select the best feature from the scalogram and 3D PRPD pattern features and to improve the recognition rate of the network. After extracting the features, the network was learned by adopting three different learning models based on the selected optimizer for the minimal loss function, i.e., root means square propagation (RMSPROP) optimizer, stochastic gradient descent with momentum (SGDM) optimizer, and adaptive momentum (ADAM) optimizer. Additionally, they used a Bayesian optimization for selecting the hyperparameters of the network.

    2.2. Fuzzy

    Fuzzy logic is introduced as many valued logic forms, and it contains multiple logical values of a variable between 0 and 1, which are partially true and partially false. Sometimes, humans cannot decide whether something is true or false in real life. Hence, the term fuzzy represents the things that are not obvious and not clear. A fuzzy algorithm gives the system some flexibility to find the best possible solution to the problem after considering all available information between the true and false values. The fuzzy logic algorithm has been used in different fields, from machine theory to artificial intelligence (AI), such as microcontrollers and workstation-based algorithms, for achieving the required output. It can also be executed in both software and hardware. In terms of HV insulators, three previous fuzzy logic approaches are discussed in this research.
    In [48], Lu, Wang et al. studied the characteristic of the ceramic pollution discharge with the use of Ultraviolet (UV) images along with the artificial climate chamber. Based on the discharge UV image, they divided the discharge type into two types. The first one is the corona discharge (CDA), and the second is the partial arc discharge area (PDA) and partial arc discharge repetition (PDR). Then, a digital image processing algorithm was applied to the UV image for segmentation purposes, the number of the partial arc discharges at specific times was counted, and the correlation of the resulting variables with the relative humidity (RH) was found. After that, the fuzzy logic inference was used, where the correlated variables are the input, and the pollution grade is the output of the technique. Wang, Lin, et al. [49] used the mean of the leakage current and environment facts in a fuzzy logic system to define the pollution condition of the HV lines. They selected a set of parameters, such as the dew-point deficit, leakage current, wind speed, and relative humidity, to be the input of the fuzzy logic system after conducting data analysis. The output of the system is pollution level, and it was linked to a webpage service. Petri, Moutinho, et al. [50] presented a method for evaluating the state of the insulators based on the severity degree generated by an instrument. The instrument consists of two main parts, the first one is the algorithm’s part, such as a fuzzy inference system or convolutional network, and the second part is the Raspberry Pi board. This instrument gives a range of severity degrees from 0 to 10, which indicates partial discharge activity. This degree was obtained using Mamdani fuzzy inference system and the extracted parameters from the partial discharge signals.

    2.3. Nero Fuzzy

    Lu, Yu et al. [51] proposed a new contamination detection technique based on a fuzzy neural network technology to overcome the drawback of the traditional detection techniques. They considered the characteristics of leakage current, relative humidity, and temperature while building the technique. Hence, as the input variables, the neural network included virtual value (Fl), leakage current peak value (Fp), temperature (T), leakage current pulse frequency (Ff), and humidity (H), and the weights of this network were constructed during the training process. The equivalent salt deposit density (ESDD), nonsoluble deposit density (NSDD), and hydrophobicity classification (HC) are the outputs of this network. In a paper [52], Khaled, El-Hag, et al. proposed a learning process for predicting the ESDD contamination level on the polymer insulators based on the recorded leakage current signals. After that, they used the stepwise regression method in the feature extraction stage and selected a set of features based on this method to feed to the learning model: salt–fog conductivity, insulator length, voltage stress, leakage current peak value for 5 h, rate of change of peak value, rate of change of average peak value, and leakage current peak value for 15 min. Then, the authors fed these features to different classifiers: KNN, polynomial, and neuro–fuzzy classifiers to specify the contamination level based on the resulting ESDD range. They also found that when they reduced the classification problem from four classes to three classes, the recognition rate increased from 65% to 78% in the polynomial classifier. In [53], Salem, Abd Rahman, et al. proposed an artificial intelligence (AI) method which combines an artificial neural network (ANN) and adaptive neuro–fuzzy inference system (ANFIS) for predicting the voltage of the pollution flashover. Data used in this method were collected from the experimental works, and the theoretical results were generated from a validated model. Diameter D, form factor F, height H, equivalent salt deposit density (ESDD), creepage distance L, and flashover voltage correction (C) are the features that were used to train the AI network for predicting the voltage values.
    Frizzo Stefenon, Zanetti Freire, et al. [54] proposed an offline time series forecasting method with an adaptive neuro–fuzzy inference system (ANFIS) to predict the insulator fault. They collected signals from the insulators using an ultrasound device. Then, they used a wavelet packet transform (WPT) to remove the noise effect in the collected signal and improve the efficiency of the time series forecasting process. They fed the extracted data into three system structures: fuzzy c-means clustering, subtractive clustering, and grid partition. They found that the wavelet neuro–fuzzy system with c-means clustering achieved the best accuracy compared with other structures.

    2.4. Detrended Fluctuation Analysis (DFA)

    In [55], Singh et al. used the detrended fluctuation analysis (DFA) on the recorded leakage current to predict the contamination level of the insulators. They observed that the DFA variable follows a specific behavior with the contamination level or the NaCl. Hence, this behavior was used to classify the contamination level, and this method showed the ability to remove the noise effect in the leakage current signal. Deb, Das, et al. [56] proposed a method for assessing the outdoor insulators based on the recorded leakage current and the use of detrended fluctuation analysis (DFA). They extracted the distortions from the recorded leakage current using a developed tracker signal based on the fundamental component; the authors found that these distortions give an indication of the contamination level of the tested insulator. Dey, Dutta, et al. [57] proposed a method based on the detrended fluctuation analysis (DFA) of the recorded leakage current to define the contamination level of the insulator. They used NaCl, Kaolin, and water to emulate the pollutant layer in the 11 kV suspension insulator disc. They showed that the DFA parameter gives a good indication of the level with respect to ESDD and conductivity.

    2.5. Miscellaneous Techniques

    Banik, Dalai, et al. [58] proposed a rough set theory (RST)-based method for classifying the contamination level of the porcelain insulators. These insulators were contaminated by the solid layer method (SLM) based on IEC60507, and the leakage current of the insulators was recorded for different levels and at different humidity values. Then, they used the autocorrelation concept for the feature selection process from the recorded leakage current since it is perfect for nonstationary leakage current and it has the ability to remove the effect of the noise in the current signal. After that, RST was applied to the extracted features to specify the contamination level of the insulator. In paper [59], Deb, Choudhury, et al. proposed a technique for predicting the contamination level on the HV lines based on the use of short-time modified Hilbert transform (STMHT) and sparse representation-based classification. They used STMHT and Fischer linear discriminant analysis (FLDA) in the feature extraction and feature reduction stages based on the recorded leakage current signals. The selected features were peak, mean, standard deviation, charge, and crest factor. These features were fed into the sparse representation-based classification model to predict the contamination level. In [60], Yan, Duan et al. proposed a method called latent low-rank representation (LatLRR) for image fusion. They collected the infrared and visible images of insulators from the HV substations under normal operation. Then, they preprocessed the visible images by guiding and filtering the images to preserve edge information in the images. This method showed that it has the ability to extract the temperature information from the infrared image so as to define the state of the insulator in the infrared image and keep the texture details of the visible image in the fusion image. So, the remaining information in the fusion image will define the contamination of the insulator. Liao, Li et al. in [61] used the technology of laser-induced breakdown spectroscopy system (LIBS) along with principal component analysis (PCA) to predict the contamination level of the insulator. The laser system was used to bombard the natural and artificial contaminated insulators with different contamination levels. The authors used a camera and spectrometer to collect the emission spectrum. The peak of the spectral line was used in specifying the element types, and then the PCA was used for classifying the spectral line into four contamination levels. In [62], de Santos and Sanz-Bobi proposed a method for predicting the leakage current of the insulator while considering the weather and environmental information of the insulator’s location. They developed a Cumulative Pollution Index (CPI) to find the soluble pollution deposit value on the insulator. The resulting value, along with wind, directional dust, and rain data, were learned using the random forests algorithm in order to determine the leakage current in the RTV silicone-coated insulators and toughened glass. In [63], Ahmad, Tahir, et al. presented a learning method for predicting the flashover parameters in the silicone rubber insulators under different values of ESDD, NSDD, humidity, and temperature. Data were collected from experimental works in the lab under controlled conditions. Four parameters and their effect on flashover voltage, arc inception, and surface resistance were studied. Cleaned data from the four parameters were trained using different learning models such as decision tree (DT), artificial neural network (ANN), least squares boosting ensemble (LSBE), polynomial support vector machine (PSVM), and Gaussian SVM (GSVM). In addition, to improve the accuracy of the model, the authors used the bootstrapping technique to increase the sample space. Zhang and Chen [64] presented a deep learning model based on the use of a deep belief network (DBN) and a sparse autoencoder (SAE) for predicting the contamination grade in the insulator. They used a double-layer stacked SAE to extract the spare features from the ultraviolet discharge images. Then, the extracted features were trained using DBN, which consists of three layers of restricted Boltzmann machine (RBM), to predict the contamination grade. Palangar and Mirzaie [65] proposed a technique for predicting the critical conditions in the glass and porcelain insulators using the leakage current. They defined a new index called by phase index; it represents the cosine of the phase angle of fundamental harmonics of the current. Based on the index, when the value is lower than 30%, the insulator is considered in an efficient state, and there is no flashover. On the other hand, when the index is higher than 30%, the insulator is put under investigation. Additionally, the authors found that when the humidity increases, the index increases accordingly. In paper [66], an innovative method was proposed to evaluate the risk of uniform and nonuniform pollution and wet glass insulator. Salem, Abd-Rahman, et al. proposed an alternative index to estimate the risk of the insulator, called Rhi, which is constructed based on the third, fifth, and seventh harmonic components of the leakage current. They tested the new index experimentally under different contamination conditions and estimated the risk of the insulator using normal and probability distribution functions (PFD). Moreover, they studied the impacts on the degree of flashover occurrence probability and the flashover voltage gradient.
    Ibrahim and Abd-Elhady [67] proposed a monitoring method for pin-type and cap and pin-type HV insulators. The method depends on the use of a low-cost Rogowski coil transducer that fits around the pin of the insulator. They analyzed the output voltage from the coil winding by using fast Fourier transform (FFT). Based on the obtained spectrum, the pollution level of the insulator can be defined directly. It was validated and tested using an experimental setup by recording the voltage and leakage current and then finding the pollution level from their spectrum using the FFT analysis. In [68], Wahyudi, Setiawan et al. investigated the ultraviolet (UV) released by a partial discharge that occurred in the dry and polluted conditions of the insulators. The intensity and UV image were recorded for one minute per voltage change per pollutant weight change, and the voltage stress was varied until the flashover happened. It was observed that there was a fixed relation between the UV emission parameters and the pollutant weight. In addition, it was noted that the UV intensity has three main values—minimum, maximum, and average—and they fluctuated in the recording stage. Based on these values, the authors showed that there were two UV-image patterns that could be identified: concentrated light and scattered points. The higher UV intensity means a higher deviation between the minimum and maximum values, and the highest concentrated light pattern was defined during a critical condition. Salem, Abd-Rahman, et al. [69] presented an innovative and alternative method to predict the pollution level of the HV insulator based on the higher component up to the seventh component of the leakage current. They formulated the new harmonic index based on the ratio of the sum of the seventh and fifth components to the third harmonic component. Next, they recorded the leakage current using a shunt resistor and current transformer. Then, a set of lab tests was conducted on porcelain and glass insulators under a salt–fog pollution state, and they are represented by three levels: light, high, and medium contamination. In a paper [70], Banik, Nielsen, et al. studied the effect of the distorted supply voltage on the leakage current of the silicone rubber insulators. They found that the supply voltage distortion causes an impact on the measured leakage current as in relative humidity and contamination severity. Hence, they proposed a crest factor-based leakage current analysis method to predict the pollution level of the insulator under distorted supply voltages. Based on the crest method, four clusters were identified with respect to the crest factor values of different insulators. Those clusters were used to define the severity level of the silicone rubber insulator.
    Castillo-Sierra, Oviedo-Trespalacios, et al. [71] presented a method for predicting and monitoring the leakage current of the polluted insulator to define the suitable washing date. The exponentially weighted moving average (EWMA) control chart was used to specify the suitable days for washing the insulator so that the washing omissions and false alarms could be reduced.

    References

    1. Maraaba, L.; Alhamouz, Z.; Alduwaish, H. A neural network-based estimation of the level of contamination on high-voltage porcelain and glass insulators. Electr. Eng. 2018, 100, 1545–1554.
    2. Abouzeid, A.K.; El-Hag, A.; Assaleh, K. Equivalent salt deposit density prediction of silicone rubber insulators under simulated pollution conditions. Electr. Power Compon. Syst. 2018, 46, 1123–1133.
    3. Kim, T.; Yi, J. Application of hydrophobic coating to reduce leakage current through surface energy control of high voltage insulator. Appl. Surf. Sci. 2022, 578, 151820.
    4. Kalla, U.K.; Suthar, R.; Sharma, K.; Singh, B.; Ghotia, J. Power quality investigation in ceramic insulator. IEEE Trans. Ind. Appl. 2017, 54, 121–134.
    5. Gençoğlu, M.T. The comparison of ceramic and non-ceramic insulators. Eng. Sci. 2007, 2, 274–294.
    6. Patel, C.R.; Patel, N.; Patel, R. Condition Assessment of Silicon Rubber Insulator used in Overhead Systems. In Proceedings of the 2020 21st National Power Systems Conference (NPSC), Gandhinagar, India, 17–19 December 2020; pp. 1–5.
    7. Rajini, H.; Ballaji, A.; Saahithi, S.; Bharat, M.; Ashwini Kumari, P.; Raghu, C. Comparative performance of insulating materials used in high voltage insulators. AIP Conf. Proc. 2022, 2461, 040001.
    8. Saleem, M.Z.; Akbar, M. Review of the performance of high-voltage composite insulators. Polymers 2022, 14, 431.
    9. Maraaba, L.S.F. Image Processing Based Contamination Level Monitdoring of High Voltage Insulator; King Fahd University of Petroleum and Minerals: Dhahran, Saudi Arabia, 2013.
    10. Maraaba, L.; Al-Hamouz, Z.; Al-Duwaish, H. Prediction of the levels of contamination of HV insulators using image linear algebraic features and neural networks. Arab. J. Sci. Eng. 2015, 40, 2609–2617.
    11. Gorur, R.; Sivasubramaniyam, S. Computation of defect-induced electric fields on outdoor high voltage ceramic and non-ceramic insulators. In Proceedings of the Annual Report Conference on Electrical Insulation and Dielectric Phenomena, Cancun, Mexico, 20–24 October 2002; pp. 319–322.
    12. Jin, L.; Zhang, D. Contamination grades recognition of ceramic insulators using fused features of infrared and ultraviolet images. Energies 2015, 8, 837–858.
    13. He, H.; Luo, D.; Lee, W.-J.; Zhang, Z.; Cao, Y.; Lu, T. A contactless insulator contamination levels detecting method based on infrared images features and RBFNN. IEEE Trans. Ind. Appl. 2018, 55, 2455–2463.
    14. Al Khafaf, N.; El-Hag, A. Bayesian regularization of neural network to predict leakage current in a salt fog environment. IEEE Trans. Dielectr. Electr. Insul. 2018, 25, 686–693.
    15. Patel, I.; Maarouf, I.; Soltan, S.; Saad, A.; Al-Taher, A.; El-Hag, A.; Assaleh, K. Image processing based estimation of ceramic insulator pollution levels. In Proceedings of the 2018 5th International Conference on Electric Power and Energy Conversion Systems (EPECS), Kitakyushu, Japan, 23–25 April 2018; pp. 1–4.
    16. Jinlei, H.; Chao, S.; Zhenxing, K.; Xiaobo, Z.; Yunpeng, J. Insulator Contamination Prediction Model Based on BP Neural Network Optimized by Genetic Algorithm. In Proceedings of the 2018 International Conference on Power System Technology (POWERCON), Guangzhou, China, 6–8 November 2018; pp. 3166–3172.
    17. Suhaimi, S.M.I.; Bashir, N.; Muhamad, N.A.; Rahim, N.N.A.; Ahmad, N.A.; Rahman, M.N.A. Surface discharge analysis of high voltage glass insulators using ultraviolet pulse voltage. Energies 2019, 12, 204.
    18. Yan, S.; Gang, H.; Jiafu, Z. The Monitoring Interface of Insulator’s State Based on the Leakage Characteristics. In Proceedings of the 2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE), Beijing, China, 6–10 September 2020; pp. 1–4.
    19. Liu, Y.; Yang, J.; Li, Y.; Pei, S.; Liu, J.; Lai, T. Research on Diagnosis Device of Insulator Pollution Degree Based on BP Neural Network. In Proceedings of the 2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES), Chengdu, China, 26–29 March 2021; pp. 166–169.
    20. Zhao, S.; Jiang, X.; Xie, Y. Evaluating the contamination level of polluted insulators based on the characteristics of leakage current. Int. Trans. Electr. Energy Syst. 2015, 25, 2109–2123.
    21. Xia, Y.; Song, X.; Jia, Z.; Wang, X.; Li, Y. Applying S-transform and SVM to evaluate insulator’s pollution condition based on leakage current. In Proceedings of the 2018 12th International conference on the properties and applications of dielectric materials (ICPADM), Xi’an, China, 20–24 May 2018; pp. 742–747.
    22. Mahdjoubi, A.; Zegnini, B.; Belkheiri, M.; Seghier, T. Fixed least squares support vector machines for flashover modelling of outdoor insulators. Electr. Power Syst. Res. 2019, 173, 29–37.
    23. Abedini-Livari, A.; Eshaghi-Maskouni, M.; Vakilian, M.; Firuzi, K. Line Composite Insulators Condition Monitoring through Partial Discharge Measurement. In Proceedings of the 2019 International Power System Conference (PSC), Tehran, Iran, 9–11 December 2019; pp. 595–600.
    24. Chen, T.; Li, F.; Wei, Z.; Li, Z. Contamination Identification and Classification on Composite Insulator by Visible Light Images. In Proceedings of the 2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE), Beijing, China, 6–10 September 2020; pp. 1–4.
    25. Liu, L.; Mei, H.; Guo, C.; Tu, Y.; Wang, L. Pixel-Level Classification of Pollution Severity on Insulators Using Photothermal Radiometry and Multiclass Semisupervised Support Vector Machine. IEEE Trans. Ind. Inf. 2020, 17, 441–449.
    26. Jin, L.; Xu, Z.; Zhang, S. A pre-warning method of contamination flashover based on the leakage current of insulators in dry condition. In Proceedings of the 2017 International Symposium on Electrical Insulating Materials (ISEIM), Toyohashi City, Japan, 11–15 September 2017; pp. 757–760.
    27. Zhou, H.; Chen, Y. Prediction of Insulator Pollution Flashover Voltage Based on Data Mining Technology. IOP Conf. Series Earth Environ. Sci. 2021, 692, 022070.
    28. Chaou, A.K.; Mekhaldi, A.; Teguar, M. Recurrence quantification analysis as a novel LC feature extraction technique for the classification of pollution severity on HV insulator model. IEEE Trans. Dielectr. Electr. Insul. 2015, 22, 3376–3384.
    29. Xia, C.; Ren, M.; Wang, B.; Dong, M.; Song, B.; Hu, Y.; Pischler, O. Acquisition and analysis of hyperspectral data for surface contamination level of insulating materials. Measurement 2021, 173, 108560.
    30. Ma, D.; Jin, L.; He, J.; Gao, K. Classification of partial discharge severities of ceramic insulators based on texture analysis of UV pulses. High Volt. 2021, 6, 986–996.
    31. Sit, K.; Das, A.K.; Mukherjee, D.; Haque, N.; Deb, S.; Pradhan, A.K.; Dalai, S.; Chatterjee, B. Condition Monitoring of Overhead Polymeric Insulators Employing Hyperbolic Window Stockwell Transform of Surface Leakage Current Signals. IEEE Sens. J. 2021, 21, 10957–10964.
    32. Corso, M.P.; Perez, F.L.; Stefenon, S.F.; Yow, K.-C.; García Ovejero, R.; Leithardt, V.R.Q. Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision. Computers 2021, 10, 112.
    33. Kannan, K.; Shivakumar, R.; Chandrasekar, S. A Random Forest Model Based Pollution Severity Classification Scheme of High Voltage Transmission Line Insulators. J. Electr. Eng. Technol. 2016, 11, 951–960.
    34. Ren, A.; Li, Q.; Xiao, H. Influence analysis and prediction of ESDD and NSDD based on random forests. Energies 2017, 10, 878.
    35. Sit, K.; Chakraborty, A.; Dalai, S.; Chatterjee, B.; Pradhan, A.K. Mathematical Morphology aided Random Forest Classifier based High Voltage Porcelain Insulator Contamination level Classification. In Proceedings of the 2020 IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh, 5–7 June 2020; pp. 98–101.
    36. Stefenon, S.F.; Grebogi, R.B.; Freire, R.Z.; Nied, A.; Meyer, L.H. Optimized ensemble extreme learning machine for classification of electrical insulators conditions. IEEE Trans. Ind. Electron. 2019, 67, 5170–5178.
    37. Qiu, Y.; Wu, G.; Xiao, Z.; Guo, Y.; Zhang, X.; Liu, K. An extreme-learning-machine-based hyperspectral detection method of insulator pollution degree. IEEE Access 2019, 7, 121156–121164.
    38. Kordkheili, H.H.; Abravesh, H.; Tabasi, M.; Dakhem, M.; Abravesh, M.M. Determining the probability of flashover occurrence in composite insulators by using leakage current harmonic components. IEEE Trans. Dielectr. Electr. Insul. 2010, 17, 502–512.
    39. Zhengfa, L.; Qing, Z.; Wuyang, Z.; Shimian, L.; Gaolin, W.; Jianlin, H.; Maoqiang, B. Study on leakage current characteristics and influence factors of 110kV polluted composite insulators. In Proceedings of the 2018 12th International Conference on the Properties and Applications of Dielectric Materials (ICPADM), Xi’an, China, 20–24 May 2018; pp. 896–900.
    40. Liu, Y.; Pei, S.; Fu, W.; Zhang, K.; Ji, X.; Yin, Z. The discrimination method as applied to a deteriorated porcelain insulator used in transmission lines on the basis of a convolution neural network. IEEE Trans. Dielectr. Electr. Insul. 2017, 24, 3559–3566.
    41. Tao, G.; Lianggang, X.; Hongyun, S.; Fengxiang, C.; Shichun, W.; Xiaowei, L. Research on Zero-Sequence Insulator Detection Technology Based on Deep Learning. J. Physics Conf. Ser. 2019, 1325, 012011.
    42. Feng, H.; Xuran, H.; Bin, L.; Haipeng, W.; Decai, Z. Infrared Image Recognition Technology Based on Visual Processing and Deep Learning. In Proceedings of the 2020 Chinese Automation Congress (CAC), Shanghai, China, 6–8 November 2020; pp. 641–645.
    43. Mussina, D.; Irmanova, A.; Jamwal, P.K.; Bagheri, M. Multi-Modal Data Fusion Using Deep Neural Network for Condition Monitoring of High Voltage Insulator. IEEE Access 2020, 8, 184486–184496.
    44. Chakraborty, S.; Podder, S.; Deb, S.; Nath, S. Qualitative Analysis of Contamination Severity between NaCl and CuSO 4 for Outdoor Insulator. In Proceedings of the 2018 IEEE Applied Signal Processing Conference (ASPCON), Kolkata, India, 7–9 December 2018; pp. 342–345.
    45. Patel, K.; Parekh, B. Prediction of flashover of silicone rubber insulator under different contaminated surface conditions. In Proceedings of the 2013 IEEE 1st International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), Kolkata, India, 6–8 December 2013; pp. 358–361.
    46. Wang, J.; Gubanski, S.M.; Blennow, J.; Atarijabarzadeh, S.; Stromberg, E.; Karlsson, S. Influence of biofilm contamination on electrical performance of silicone rubber based composite materials. IEEE Trans. Dielectr. Electr. Insul. 2012, 19, 1690–1699.
    47. Vigneshwaran, B.; Maheswari, R.; Kalaivani, L.; Shanmuganathan, V.; Rho, S.; Kadry, S.; Lee, M.Y. Recognition of pollution layer location in 11 kV polymer insulators used in smart power grid using dual-input VGG Convolutional Neural Network. Energy Rep. 2021, 7, 7878–7889.
    48. Lu, F.; Wang, S.; Li, H. Insulator pollution grade evaluation based on ultraviolet imaging and fuzzy logic inference. In Proceedings of the 2010 Asia-Pacific Power and Energy Engineering Conference, Chengdu, China, 28–31 March 2010; pp. 1–4.
    49. Wang, Y.-C.; Lin, Y.-T.; Chang, H.-C.; Kuo, C.-C. Contamination assessment of insulators using microsystem technology with fuzzy-based approach. Microsyst. Technol. 2019, 27, 1759–1772.
    50. Petri, L.d.P.S.; Moutinho, E.A.; Silva, R.P.; Capelini, R.M.; Salustiano, R.; Ferraz, G.M.F.; Neto, E.T.W.; Villibor, J.P.; Pinto, S.S. A Portable System for the Evaluation of the Degree of Pollution of Transmission Line Insulators. Energies 2020, 13, 6625.
    51. Lu, Y.-P.; Yu, M.; Lai, L.; Lin, X. A new fuzzy neural network based insulator contamination detection. In Proceedings of the 2006 International Conference on Machine Learning and Cybernetics, Dalian, China, 13–16 August 2006; pp. 4099–4104.
    52. Khaled, A.; El-Hag, A.; Assaleh, K. Equivalent salt deposit density prediction of outdoor polymer insulators during salt fog test. In Proceedings of the 2016 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP), Toronto, ON, Canada, 16–19 October 2016; pp. 786–789.
    53. Salem, A.A.; Abd Rahman, R.; Kamarudin, M.; Othman, N.; Jamail, N.; Hamid, H.; Ishak, M. An alternative approaches to predict flashover voltage on polluted outdoor insulators using artificial intelligence techniques. Bull. Electr. Eng. Inform. 2020, 9, 533–541.
    54. Frizzo Stefenon, S.; Zanetti Freire, R.; dos Santos Coelho, L.; Meyer, L.H.; Bartnik Grebogi, R.; Gouvêa Buratto, W.; Nied, A. Electrical insulator fault forecasting based on a wavelet neuro-fuzzy system. Energies 2020, 13, 484.
    55. Singh, P.; Dutta, S.; Baral, A.; Chakravorti, S. Contamination Level Assessment in Porcelain Disc Insulator using Detrended Fluctuation Analysis. In Proceedings of the 2019 IEEE 4th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), Chennai, India, 21–23 November 2019; pp. 1–5.
    56. Deb, S.; Das, S.; Pradhan, A.K.; Banik, A.; Chatterjee, B.; Dalai, S. Estimation of contamination level of overhead insulators based on surface leakage current employing detrended fluctuation analysis. IEEE Trans. Ind. Electron. 2019, 67, 5729–5736.
    57. Dey, J.; Dutta, S.; Baral, A.; Chakravorti, S. Leakage Current Monitoring of Suspension Insulator for Effective Determination of ESDD. In Proceedings of the 2019 8th International Conference on Power Systems (ICPS), Jaipur, India, 20–22 December 2019; pp. 1–6.
    58. Banik, A.; Dalai, S.; Chatterjee, B. Autocorrelation aided rough set based contamination level prediction of high voltage insulator at different environmental condition. IEEE Trans. Dielectr. Electr. Insul. 2016, 23, 2883–2891.
    59. Deb, S.; Choudhury, N.R.; Ghosh, R.; Chatterjee, B.; Dalai, S. Short time modified Hilbert transform-aided sparse representation for sensing of overhead line insulator contamination. IEEE Sens. J. 2018, 18, 8125–8132.
    60. Yan, S.J.; Duan, W.S.; Shan, H.T.; Tong, M.S. Insulator Contamination Measurement Based on Infrared Thermal and Visible Image Information Fusion. In Proceedings of the 2019 PhotonIcs & Electromagnetics Research Symposium-Spring (PIERS-Spring), Rome, Italy, 17–20 June 2019; pp. 1006–1011.
    61. Liao, Y.; Li, Y.; Zhang, F.; Zhang, X.; Wang, T.; Guo, Y.; Xiao, Z.; Wang, Y. Study on evaluation method of insulator surface contamination level based on LIBS technology and PCA algorithm. In Proceedings of the 2019 2nd International Conference on Electrical Materials and Power Equipment (ICEMPE), Guangzhou, China, 7–10 April 2019; pp. 512–517.
    62. de Santos, H.; Sanz-Bobi, M.Á. A Cumulative Pollution Index for the Estimation of the Leakage Current on Insulator Strings. IEEE Trans. Power Deliv. 2020, 35, 2438–2446.
    63. Ahmad, J.; Tahir, A.; Stewart, B.G.; Nekahi, A. Forecasting Flashover Parameters of Polymeric Insulators under Contaminated Conditions Using the Machine Learning Technique. Energies 2020, 13, 3889.
    64. Zhang, D.; Chen, S. Intelligent Recognition of Insulator Contamination Grade Based on the Deep Learning of Ultraviolet Discharge Image Information. Energies 2020, 13, 5221.
    65. Palangar, M.; Mirzaie, M. Predicting Critical Conditions in Polluted Insulators Using Phase Angle Index of Leakage Current. In Proceedings of the 2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE), Beijing, China, 6–10 September 2020; pp. 1–4.
    66. Salem, A.A.; Abd-Rahman, R.; Al-Gailani, S.A.; Salam, Z.; Kamarudin, M.S.; Zainuddin, H.; Yousof, M.F.M. Risk assessment of polluted glass insulator using leakage current index under different operating conditions. IEEE Access 2020, 8, 175827–175839.
    67. Ibrahim, M.E.; Abd-Elhady, A.M. Rogowski Coil Transducer-Based Condition Monitoring of High Voltage Insulators. IEEE Sens. J. 2020, 20, 13694–13703.
    68. Wahyudi, M.; Setiawan, N.A.; Pambudi, K.; Saputra, D. Severity Level of An Insulator in Polluted and Dry Conditions Based on Ultraviolet Emission. In Proceedings of the 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES), Bangkok, Thailand, 2–4 June 2020; pp. 457–462.
    69. Salem, A.A.; Abd-Rahman, R.; Al-Gailani, S.A.; Kamarudin, M.S.; Ahmad, H.; Salam, Z. The leakage current components as a diagnostic tool to estimate contamination level on high voltage insulators. IEEE Access 2020, 8, 92514–92528.
    70. Banik, A.; Nielsen, S.; Nourbakhsh, G. A crest factor-based technique for the analysis of polluted insulator leakage current under harmonically distorted supply voltage. Electr. Eng. 2021, 103, 1823–1836.
    71. Castillo-Sierra, R.; Oviedo-Trespalacios, O.; Candelo-Becerra, J.E.; Soto, J.D.; Calle, M. A novel method for prediction of washing cycles of electrical insulators in high pollution environments. Int. J. Electr. Power Energy Syst. 2021, 130, 107026.
    More
    Information
    Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : , , , , ,
    View Times: 44
    Revisions: 2 times (View History)
    Update Time: 15 Nov 2022
    Table of Contents
      1000/1000

      Confirm

      Are you sure to Delete?

      Video Upload Options

      Do you have a full video?
      Cite
      If you have any further questions, please contact Encyclopedia Editorial Office.
      Maraaba, L.; Al-Soufi, K.; Ssennoga, T.; Memon, A.M.; Worku, M.Y.; Alhems, L.M. Major High-Voltage Insulator Contamination Level Classification Techniques. Encyclopedia. Available online: https://encyclopedia.pub/entry/34449 (accessed on 01 December 2022).
      Maraaba L, Al-Soufi K, Ssennoga T, Memon AM, Worku MY, Alhems LM. Major High-Voltage Insulator Contamination Level Classification Techniques. Encyclopedia. Available at: https://encyclopedia.pub/entry/34449. Accessed December 01, 2022.
      Maraaba, Luqman, Khaled Al-Soufi, Twaha Ssennoga, Azhar M. Memon, Muhammed Y. Worku, Luai M. Alhems. "Major High-Voltage Insulator Contamination Level Classification Techniques," Encyclopedia, https://encyclopedia.pub/entry/34449 (accessed December 01, 2022).
      Maraaba, L., Al-Soufi, K., Ssennoga, T., Memon, A.M., Worku, M.Y., & Alhems, L.M. (2022, November 14). Major High-Voltage Insulator Contamination Level Classification Techniques. In Encyclopedia. https://encyclopedia.pub/entry/34449
      Maraaba, Luqman, et al. ''Major High-Voltage Insulator Contamination Level Classification Techniques.'' Encyclopedia. Web. 14 November, 2022.
      Top
      Feedback