Design of a Condition Monitoring System for Wind Turbines

Jinje Park, Changhyun Kim, Minh-Chau Dinh and Minwon Park
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Jinje Park: Department of Electrical Engineering, Changwon National University, Changwon 51140, Korea
Changhyun Kim: Department of Electrical Engineering, Changwon National University, Changwon 51140, Korea
Minh-Chau Dinh: Institute of Mechatronics, Changwon National University, Changwon 51140, Korea
Minwon Park: Department of Electrical Engineering, Changwon National University, Changwon 51140, Korea

Energies, 2022, vol. 15, issue 2, 1-16

Abstract: 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.

Keywords: correlation analysis; artificial neural network; machine learning; operations and maintenance; wind turbine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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