2018 12th International Conference on the Properties and Applications of Dielectric Materials (ICPADM) 2018
DOI: 10.1109/icpadm.2018.8401131
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Applying S-transform and SVM to evaluate insulator's pollution condition based on leakage current

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Cited by 5 publications
(2 citation statements)
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“…From a literature survey, it is evident that insulator contamination is related to weather parameters such as temperature, relative humidity, pressure, wind speed, and ultraviolet [13][14][15][16]. Since the leakage current on the surface of the insulator is affected by the material, surface contamination, and surrounding environment, as well as its nonlinear characteristics, artificial intelligence algorithms such as machine learning technology have been applied to analyze the leakage current or estimate contamination on the surface of insulators in some related studies [10,11,13,[17][18][19][20][21][22]. For instance, artificial neural networks (ANNs) have been used to build a leakage current model [18,19], support vector machines (SVMs) to evaluate contamination degree [20,21], and random forests to predict equivalent salt deposit density (ESDD) based on parameters such as pollution and weather [22].…”
Section: Leakage Current Rangementioning
confidence: 99%
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“…From a literature survey, it is evident that insulator contamination is related to weather parameters such as temperature, relative humidity, pressure, wind speed, and ultraviolet [13][14][15][16]. Since the leakage current on the surface of the insulator is affected by the material, surface contamination, and surrounding environment, as well as its nonlinear characteristics, artificial intelligence algorithms such as machine learning technology have been applied to analyze the leakage current or estimate contamination on the surface of insulators in some related studies [10,11,13,[17][18][19][20][21][22]. For instance, artificial neural networks (ANNs) have been used to build a leakage current model [18,19], support vector machines (SVMs) to evaluate contamination degree [20,21], and random forests to predict equivalent salt deposit density (ESDD) based on parameters such as pollution and weather [22].…”
Section: Leakage Current Rangementioning
confidence: 99%
“…Since the leakage current on the surface of the insulator is affected by the material, surface contamination, and surrounding environment, as well as its nonlinear characteristics, artificial intelligence algorithms such as machine learning technology have been applied to analyze the leakage current or estimate contamination on the surface of insulators in some related studies [10,11,13,[17][18][19][20][21][22]. For instance, artificial neural networks (ANNs) have been used to build a leakage current model [18,19], support vector machines (SVMs) to evaluate contamination degree [20,21], and random forests to predict equivalent salt deposit density (ESDD) based on parameters such as pollution and weather [22]. Although there have been numerous studies using machine learning and other algorithms for the leakage current of insulators, there is limited study on modeling analysis using long-term measurement data to build predictive models and evaluate the most effective prediction methods.…”
Section: Leakage Current Rangementioning
confidence: 99%