Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators
The most common method for inspection of insulators is to measure the change of electrical characteristics such as electric resistance and partial discharge. However, even if there is no physical damage, these values vary depending on the temperature, humidity, and chloride content of the atmosphere...
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MDPI AG
2020-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/8/2820 |
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| author | In Hyuk Choi Ja Bin Koo Ju Am Son Jun Sin Yi Young Geun Yoon Tae Keun Oh |
| author_facet | In Hyuk Choi Ja Bin Koo Ju Am Son Jun Sin Yi Young Geun Yoon Tae Keun Oh |
| author_sort | In Hyuk Choi |
| collection | DOAJ |
| description | The most common method for inspection of insulators is to measure the change of electrical characteristics such as electric resistance and partial discharge. However, even if there is no physical damage, these values vary depending on the temperature, humidity, and chloride content of the atmosphere. In this respect, an alternative to such methods can be the impact response test, and a frequency response function (FRF) obtained from the test has been widely used as a tool for damage detection. In this study the FRF was applied to identify the cap damage of porcelain insulators. In addition, to solve the danger of high voltage and poor field accessibility near the insulator, a device with high field applicability was developed to measure FRF from a long distance using an auto impact hammer and Micro Electro Mechanical Systems (MEMS) technology. Even though the FRF is most suitable for inspection of porcelain insulators, dynamic characteristics such as natural frequencies may vary depending on manufacturing errors, installation conditions, etc., which may cause difficulties in damage identification. To overcome this limitation, the machine learning (ML) method was applied in this study to provide a diagnostic method that ensured consistent and accurate judgment. As a result of predicting the normal and the cap damage data using the support vector machine (SVM), bagging, k-nearest neighbor (kNN), and discriminant analysis (DA) methods, the overall F1 score was over 87% and the bagging method achieved the highest accuracy. In this study, the frequency range and dynamic characteristics that are sensitive to the physical damage of the insulator were derived and, based on this, the optimum ML methods with improved equipment could provide analysis with higher accuracy and consistency than general analysis using the FRF. |
| first_indexed | 2024-03-10T20:22:18Z |
| format | Article |
| id | doaj.art-890a6fbb057e4d87af7f0eb0d5657b23 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T20:22:18Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-890a6fbb057e4d87af7f0eb0d5657b232023-11-19T22:05:16ZengMDPI AGApplied Sciences2076-34172020-04-01108282010.3390/app10082820Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain InsulatorsIn Hyuk Choi0Ja Bin Koo1Ju Am Son2Jun Sin Yi3Young Geun Yoon4Tae Keun Oh5KEPCO Research Institute, Daejeon 34056, KoreaKEPCO Research Institute, Daejeon 34056, KoreaKEPCO Research Institute, Daejeon 34056, KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, KoreaDepartment of Safety Engineering, Incheon National University, Incheon 22012, KoreaDepartment of Safety Engineering, Incheon National University, Incheon 22012, KoreaThe most common method for inspection of insulators is to measure the change of electrical characteristics such as electric resistance and partial discharge. However, even if there is no physical damage, these values vary depending on the temperature, humidity, and chloride content of the atmosphere. In this respect, an alternative to such methods can be the impact response test, and a frequency response function (FRF) obtained from the test has been widely used as a tool for damage detection. In this study the FRF was applied to identify the cap damage of porcelain insulators. In addition, to solve the danger of high voltage and poor field accessibility near the insulator, a device with high field applicability was developed to measure FRF from a long distance using an auto impact hammer and Micro Electro Mechanical Systems (MEMS) technology. Even though the FRF is most suitable for inspection of porcelain insulators, dynamic characteristics such as natural frequencies may vary depending on manufacturing errors, installation conditions, etc., which may cause difficulties in damage identification. To overcome this limitation, the machine learning (ML) method was applied in this study to provide a diagnostic method that ensured consistent and accurate judgment. As a result of predicting the normal and the cap damage data using the support vector machine (SVM), bagging, k-nearest neighbor (kNN), and discriminant analysis (DA) methods, the overall F1 score was over 87% and the bagging method achieved the highest accuracy. In this study, the frequency range and dynamic characteristics that are sensitive to the physical damage of the insulator were derived and, based on this, the optimum ML methods with improved equipment could provide analysis with higher accuracy and consistency than general analysis using the FRF.https://www.mdpi.com/2076-3417/10/8/2820frequency response functionporcelain insulatorcapauto impact hammerMEMSnon-destructive method |
| spellingShingle | In Hyuk Choi Ja Bin Koo Ju Am Son Jun Sin Yi Young Geun Yoon Tae Keun Oh Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators Applied Sciences frequency response function porcelain insulator cap auto impact hammer MEMS non-destructive method |
| title | Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators |
| title_full | Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators |
| title_fullStr | Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators |
| title_full_unstemmed | Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators |
| title_short | Development of Equipment and Application of Machine Learning Techniques Using Frequency Response Data for Cap Damage Detection of Porcelain Insulators |
| title_sort | development of equipment and application of machine learning techniques using frequency response data for cap damage detection of porcelain insulators |
| topic | frequency response function porcelain insulator cap auto impact hammer MEMS non-destructive method |
| url | https://www.mdpi.com/2076-3417/10/8/2820 |
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