Design of a Condition Monitoring System for Wind Turbines
Renewable energy is being adopted worldwide, and the proportion of offshore wind turbines is increasing. Offshore wind turbines operate in harsh weather conditions, resulting in various failures and high maintenance costs. In this paper, a condition diagnosis model for condition monitoring of an off...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/2/464 |
| _version_ | 1797494366363713536 |
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| author | Jinje Park Changhyun Kim Minh-Chau Dinh Minwon Park |
| author_facet | Jinje Park Changhyun Kim Minh-Chau Dinh Minwon Park |
| author_sort | Jinje Park |
| collection | DOAJ |
| description | Renewable energy is being adopted worldwide, and the proportion of offshore wind turbines is increasing. Offshore wind turbines operate in harsh weather conditions, resulting in various failures and high maintenance costs. In this paper, a condition diagnosis model for condition monitoring of an offshore wind turbine has been developed. The generator, main bearing, pitch system, and yaw system were selected as components subject to the condition monitoring by considering the failure rate and downtime of the wind turbine. The condition diagnosis model works by comparing real-time and predictive operating data of the wind turbine, and about four years of Supervisory Control and Data Acquisition (SCADA) data from a 2 MW wind turbine was used to develop the model. A deep neural network and an artificial neural network were used as machine learning to predict the operational data in the condition diagnosis model, and a confusion matrix was used to measure the accuracy of the failure determination. As a result of the condition monitoring derived by inputting SCADA data to the designed system, it was possible to maintain the failure determination accuracy of more than 90%. The proposed condition monitoring system will be effectively utilized for the maintenance of wind turbines. |
| first_indexed | 2024-03-10T01:33:14Z |
| format | Article |
| id | doaj.art-15dcd466f47842ed93f98a60d35bc8a4 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T01:33:14Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-15dcd466f47842ed93f98a60d35bc8a42023-11-23T13:36:32ZengMDPI AGEnergies1996-10732022-01-0115246410.3390/en15020464Design of a Condition Monitoring System for Wind TurbinesJinje Park0Changhyun Kim1Minh-Chau Dinh2Minwon Park3Department of Electrical Engineering, Changwon National University, Changwon 51140, KoreaDepartment of Electrical Engineering, Changwon National University, Changwon 51140, KoreaInstitute of Mechatronics, Changwon National University, Changwon 51140, KoreaDepartment of Electrical Engineering, Changwon National University, Changwon 51140, KoreaRenewable energy is being adopted worldwide, and the proportion of offshore wind turbines is increasing. Offshore wind turbines operate in harsh weather conditions, resulting in various failures and high maintenance costs. In this paper, a condition diagnosis model for condition monitoring of an offshore wind turbine has been developed. The generator, main bearing, pitch system, and yaw system were selected as components subject to the condition monitoring by considering the failure rate and downtime of the wind turbine. The condition diagnosis model works by comparing real-time and predictive operating data of the wind turbine, and about four years of Supervisory Control and Data Acquisition (SCADA) data from a 2 MW wind turbine was used to develop the model. A deep neural network and an artificial neural network were used as machine learning to predict the operational data in the condition diagnosis model, and a confusion matrix was used to measure the accuracy of the failure determination. As a result of the condition monitoring derived by inputting SCADA data to the designed system, it was possible to maintain the failure determination accuracy of more than 90%. The proposed condition monitoring system will be effectively utilized for the maintenance of wind turbines.https://www.mdpi.com/1996-1073/15/2/464correlation analysisartificial neural networkmachine learningoperations and maintenancewind turbine |
| spellingShingle | Jinje Park Changhyun Kim Minh-Chau Dinh Minwon Park Design of a Condition Monitoring System for Wind Turbines Energies correlation analysis artificial neural network machine learning operations and maintenance wind turbine |
| title | Design of a Condition Monitoring System for Wind Turbines |
| title_full | Design of a Condition Monitoring System for Wind Turbines |
| title_fullStr | Design of a Condition Monitoring System for Wind Turbines |
| title_full_unstemmed | Design of a Condition Monitoring System for Wind Turbines |
| title_short | Design of a Condition Monitoring System for Wind Turbines |
| title_sort | design of a condition monitoring system for wind turbines |
| topic | correlation analysis artificial neural network machine learning operations and maintenance wind turbine |
| url | https://www.mdpi.com/1996-1073/15/2/464 |
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