Data-Driven Condition Monitoring Approaches to Improving Power Output of Wind Turbines

• Published in: IEEE transactions on industrial electronics (1982) Vol. 66; no. 8; pp. 6012 - 6020
• Main Authors: Qian, Peng, Ma, Xiandong, Zhang, Dahai, Wang, Junheng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analytic hierarchy process; Analytic hierarchy process (AHP); Bonferroni interval; Condition monitoring; condition monitoring (CM); Estimation; extreme learning machine (ELM); Fatigue; Gearboxes; Generators; Health; health condition estimation; Machine learning; Maintenance engineering; Neural networks; Optimization; Power dispatch; Supervisory control and data acquisition; Turbines; Wind farms; Wind power; Wind power generation; Wind turbines
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2018.2873519

This paper presents data-driven approaches to improve the active power output of wind turbines based on estimating their health condition. The main procedure includes estimations of fault degree and health condition level, and optimal power dispatch control. The proposed method can adjust the active power output of individual turbines according to their health condition and can thus optimize the total energy output of a wind farm. In the paper, extreme learning machine algorithm and Bonferroni interval are applied to estimate fault degree, while an analytic hierarchy process is used to estimate the health condition level. A scheme for power dispatch control is formulated based on the estimated health condition. Models have been identified from supervisory control and data acquisition data acquired from an operational wind farm, which contains temperature data of gearbox bearing and generator winding. The results show that the proposed method can maximize the operation efficiency of the wind farm while significantly reducing the fatigue loading on the faulty wind turbines.