Damage Evaluation of Porcelain Insulators with 154 kV Transmission Lines by Various Support Vector Machine (SVM) and Ensemble Methods Using Frequency Response Data

Choi, In Hyuk; Koo, Ja Bin; Woo, Jung Wook; Son, Ju Am; Bae, Do Yeon; Yoon, Young-Geun; Oh, Tae Keun

doi:10.3390/app10010084

Cited by 9 publications

(6 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, these discharge activities can escalate and generate a flashover occurrence, which might cause an electrical grid outage or even breakdown [7][8][9] . Therefore, it is necessary to monitor and assess the health condition of insulators in order to ensure the integrity of the insulator and the transmission system as a whole 10,11 . This improves the grid's performance and reduces the likelihood of a malfunction.…”

Section: Leakage Current Characteristics In Estimating Insulator Reli...mentioning

confidence: 99%

Leakage current characteristics in estimating insulator reliability: experimental investigation and analysis

Salem

Lau

Rahiman

et al. 2022

Sci Rep

View full text Add to dashboard Cite

The monitoring of leakage current (LC) and voltage characteristics in transmission line insulators is regarded as a good technique for anticipating the physical state of in-service insulators. In the current work, the temporal and frequency characteristics of LC and voltage under various situations were derived for assessing the health condition of porcelain, glass, and silicone rubber insulators. The contamination severity indicated by soluble deposit density, wetting level (Wt), non-soluble deposit density, and uneven pollution distribution (Pu/PL) were chosen as the environmental factors that impact the insulators. Six criteria were utilized to evaluate the physical state of the insulators, with four of those derived from the LC signal in the time domain, namely, the LC signal peak (C1), the phase shift between applied voltage and LC (C2), the LC signal slope between two consecutive peaks (C3), and the crest factor (C4). The remaining two indices, namely, the total harmonics distribution (C5) and the harmonics ratio indicator (C6), were obtained from the frequency domain of the LC signal. In addition, the flashover voltage index (C7) was also employed. The LC indicators were then classified based on the laboratory test results to reflect the physical state of the insulators. The findings revealed that the proposed indicators had an important impact in determining the physical state of the insulators. Furthermore, a confusion matrix was created for the test and prediction data using the suggested indicators to determine the effectiveness of each indicator.

show abstract

Section: Leakage Current Characteristics In Estimating Insulator Reli...mentioning

confidence: 99%

Leakage current characteristics in estimating insulator reliability: experimental investigation and analysis

Salem

Lau

Rahiman

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…These discharges can develop into an unwanted flashover that may disrupt an electrical grid [5][6][7][8]. Therefore, monitoring the condition of insulators has a significant effect on the power system stability [9,10]. The assessment of outdoor insulator properties and long-term efficiency is thus an important area of research in the science establishment [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Polymeric Insulator Conditions Estimation by Using Leakage Current Characteristics Based on Simulation and Experimental Investigation

et al. 2022

View full text Add to dashboard Cite

The current work contributes an estimate of the time-frequency characteristics of a leakage current in assessing the health condition of a polluted polymeric insulator. A 33 kV polymer insulator string was subjected to a series of laboratory tests under a range of environmental conditions, including pollution, wetting rate (WR), non-soluble deposit density (NSDD), and non-uniform distribution pollution (FT/B). The temporal and frequency features of the leakage current were then extracted and used as assessment indicators for insulator conditions based on laboratory test findings. Two indices were generated from the leakage current waveform in the time domain: the curve slope index (F1), which is determined by measuring the inclination of the curve between two successive time peaks of the leakage current, and the crest factor indicator (F2). The frequency domain of the leakage current signal was used to calculate the other two indices. These are the odd harmonic indicators derived from the odd frequency harmonics of the leakage current up to the 9th component (F3) and the 5th to 3rd harmonics ratio (F4). The findings showed that the suggested indicators were capable of evaluating insulator conditions. Finally, the confusion matrix for the experimental and prediction results obtained with the proposed indices was used to assess which indicator performed the best. Therefore, the analysis suggests an alternative and effective method for estimating the health condition of a polluted insulator through leakage current characteristics obtained in the time and frequency domains.

show abstract

“…Support Vector Machine is a widely popular model of machine learning. A hyper-plane has been generated using the training datasets when converting from the actual support vector machine datasets to high dimensional feature space has occurred (Choi et al 2020). The functional performance of the model depends mainly on the usage of a suitable kernel.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model

Yaseen

Chen

2022

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

Floods are among the most destructive natural hazards. Therefore, their prediction is pivotal for flood management and public safety. Factors contributing to flooding are different for every region as they depend upon the characteristics of each region. Therefore, this study evaluated the factors contributing to flood and the precise location of high and very high flood susceptibility regions in Karachi. A new ensemble model (LR-SVM-MLP) is introduced to develop the susceptibility map and evaluate influencing factors. This ensemble model was formed by employing a stacking ensemble on Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). A spatial database was generated for the Karachi watershed, which included; twelve conditioning factors as independent variables, 652 flood points and the same number of non-flood points as dependent variables. This data was then randomly divided into 70% and 30% to train and validate models, respectively. To analyse the collinearity among factors and to scrutinize each variable's predictive power, multicollinearity test and Information Gain Ratio were applied, respectively. After training, the models were evaluated on various statistical measures and compared with benchmark models. Results revealed that the proposed ensemble model outperformed Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) and produced a precise and accurate map. Results of the ensemble model showed 99% accuracy in training and 98% accuracy in validation datasets. This ensemble model can be used by flood management authorities and the government to contribute to future research studies.

show abstract

Damage Evaluation of Porcelain Insulators with 154 kV Transmission Lines by Various Support Vector Machine (SVM) and Ensemble Methods Using Frequency Response Data

Cited by 9 publications

References 30 publications

Leakage current characteristics in estimating insulator reliability: experimental investigation and analysis

Leakage current characteristics in estimating insulator reliability: experimental investigation and analysis

Polymeric Insulator Conditions Estimation by Using Leakage Current Characteristics Based on Simulation and Experimental Investigation

Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model

Contact Info

Product

Resources

About