Characterization of the impact of coating defects on ceramic insulator efficiency: Machine learning approaches
Porcelain insulators are essential for the safety and efficiency of electrical systems, yet their hydrophobicity needs improvement through anti-pollution flashover coatings. However, these coatings are prone to damage from factors such as pollution. This study aims to investigate the electrical char...
Published in: | ELECTRIC POWER SYSTEMS RESEARCH |
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Main Authors: | , , , , , , , |
Format: | Article |
Language: | English |
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ELSEVIER SCIENCE SA
2025
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Online Access: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001349283600001 |
author |
Salem Ali Ahmed; Alawady Ahmed Allawy; AL-Gailani Samir A.; Amer Abdulrahman Ahmed Ghaleb; Al-Ameri Salem Mgammal; Abd-Rahman Rahisham; AL-Gailani Nasir Ahmed |
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Salem Ali Ahmed; Alawady Ahmed Allawy; AL-Gailani Samir A.; Amer Abdulrahman Ahmed Ghaleb; Al-Ameri Salem Mgammal; Abd-Rahman Rahisham; AL-Gailani Nasir Ahmed Characterization of the impact of coating defects on ceramic insulator efficiency: Machine learning approaches Engineering |
author_facet |
Salem Ali Ahmed; Alawady Ahmed Allawy; AL-Gailani Samir A.; Amer Abdulrahman Ahmed Ghaleb; Al-Ameri Salem Mgammal; Abd-Rahman Rahisham; AL-Gailani Nasir Ahmed |
author_sort |
Salem |
spelling |
Salem, Ali Ahmed; Alawady, Ahmed Allawy; AL-Gailani, Samir A.; Amer, Abdulrahman Ahmed Ghaleb; Al-Ameri, Salem Mgammal; Abd-Rahman, Rahisham; AL-Gailani, Nasir Ahmed Characterization of the impact of coating defects on ceramic insulator efficiency: Machine learning approaches ELECTRIC POWER SYSTEMS RESEARCH English Article Porcelain insulators are essential for the safety and efficiency of electrical systems, yet their hydrophobicity needs improvement through anti-pollution flashover coatings. However, these coatings are prone to damage from factors such as pollution. This study aims to investigate the electrical characteristics, specifically flashover voltage and leakage current, of insulator strings under seven different RTV-coating damage scenarios, as well as fully coated and bare scenarios, in the presence of pollution and humidity. Machine learning models were utilized to estimate the condition of insulators under these coating damage scenarios. Statistical features were extracted from the leakage current (LC) and flashover voltage (Uf) of the insulators using signal analyzer techniques. These features were then used to train machine-learning algorithms, including Support Vector Machines (SVM), Multi-layer Perceptron (MLP), and the Random Under Sampling with Adaptive Boosting algorithm (RUSBoost). A Self-Organizing Map (SOM) was employed to cluster the flashover voltage and leakage current datasets before training the machine learning models. The results indicated that risky insulator conditions were more prevalent in bare insulators and those with severe RTV coating deterioration, such as scenarios S5 and S8. The proposed models effectively identified and classified the conditions of insulator strings under various coating damage scenarios, demonstrating strong performance metrics. The use of statistical features improved classifier performance by up to 6.37 %. This approach offers a robust, automated method for the maintenance and monitoring of ceramic insulators, potentially reducing inspection time and enhancing the reliability of electrical infrastructure. ELSEVIER SCIENCE SA 0378-7796 1873-2046 2025 238 10.1016/j.epsr.2024.111158 Engineering WOS:001349283600001 https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001349283600001 |
title |
Characterization of the impact of coating defects on ceramic insulator efficiency: Machine learning approaches |
title_short |
Characterization of the impact of coating defects on ceramic insulator efficiency: Machine learning approaches |
title_full |
Characterization of the impact of coating defects on ceramic insulator efficiency: Machine learning approaches |
title_fullStr |
Characterization of the impact of coating defects on ceramic insulator efficiency: Machine learning approaches |
title_full_unstemmed |
Characterization of the impact of coating defects on ceramic insulator efficiency: Machine learning approaches |
title_sort |
Characterization of the impact of coating defects on ceramic insulator efficiency: Machine learning approaches |
container_title |
ELECTRIC POWER SYSTEMS RESEARCH |
language |
English |
format |
Article |
description |
Porcelain insulators are essential for the safety and efficiency of electrical systems, yet their hydrophobicity needs improvement through anti-pollution flashover coatings. However, these coatings are prone to damage from factors such as pollution. This study aims to investigate the electrical characteristics, specifically flashover voltage and leakage current, of insulator strings under seven different RTV-coating damage scenarios, as well as fully coated and bare scenarios, in the presence of pollution and humidity. Machine learning models were utilized to estimate the condition of insulators under these coating damage scenarios. Statistical features were extracted from the leakage current (LC) and flashover voltage (Uf) of the insulators using signal analyzer techniques. These features were then used to train machine-learning algorithms, including Support Vector Machines (SVM), Multi-layer Perceptron (MLP), and the Random Under Sampling with Adaptive Boosting algorithm (RUSBoost). A Self-Organizing Map (SOM) was employed to cluster the flashover voltage and leakage current datasets before training the machine learning models. The results indicated that risky insulator conditions were more prevalent in bare insulators and those with severe RTV coating deterioration, such as scenarios S5 and S8. The proposed models effectively identified and classified the conditions of insulator strings under various coating damage scenarios, demonstrating strong performance metrics. The use of statistical features improved classifier performance by up to 6.37 %. This approach offers a robust, automated method for the maintenance and monitoring of ceramic insulators, potentially reducing inspection time and enhancing the reliability of electrical infrastructure. |
publisher |
ELSEVIER SCIENCE SA |
issn |
0378-7796 1873-2046 |
publishDate |
2025 |
container_volume |
238 |
container_issue |
|
doi_str_mv |
10.1016/j.epsr.2024.111158 |
topic |
Engineering |
topic_facet |
Engineering |
accesstype |
|
id |
WOS:001349283600001 |
url |
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001349283600001 |
record_format |
wos |
collection |
Web of Science (WoS) |
_version_ |
1818940500806205440 |