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

Frequency response signals have been used for the non-destructive evaluation of many different structures and for the integrity evaluation of porcelain insulators. However, it is difficult to accurately estimate the integrity of porcelain insulators under various environmental conditions only by usi...

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Bibliographic Details
Published in:Applied sciences 2020-01, Vol.10 (1), p.84
Main Authors: Choi, In Hyuk, Koo, Ja Bin, Woo, Jung Wook, Son, Ju Am, Bae, Do Yeon, Yoon, Young Geun, Oh, Tae Keun
Format: Article
Language:English
Subjects:
Accident prevention
Accuracy
Artificial intelligence
Classification
Damage assessment
Environmental conditions
Evaluation
Frequency dependence
Frequency response
Insulators
Learning algorithms
Machine learning
Nondestructive testing
Porcelain
Principal components analysis
Support vector machines
Transmission lines
Trends
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Summary:Frequency response signals have been used for the non-destructive evaluation of many different structures and for the integrity evaluation of porcelain insulators. However, it is difficult to accurately estimate the integrity of porcelain insulators under various environmental conditions only by using general frequency response signals. Therefore, this study used a method that extracted several features that can be derived from the frequency response signal and reduced their dimensions to select features suitable for the evaluation of the soundness of porcelain insulators. The latest machine learning techniques were used to identify correlations and not for basic feature analyses. Two machine learning models were developed using the support vector machine and ensemble methods in MATLAB. Both models showed high reliability in distinguishing between normal and defective porcelain insulators, and they could visualize the distribution area of the data by extracting quantitative values and applying machine learning, rather than simply verifying the frequency response signal.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10010084