Wind Turbine Pitch System Condition Monitoring and Fault Detection Based on Optimized Relevance Vector Machine Regression

• Published in: IEEE transactions on sustainable energy Vol. 11; no. 4; pp. 2326 - 2336
• Main Authors: Wei, Lu, Qian, Zheng, Zareipour, Hamidreza
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive threshold; Anomalies; Computer applications; Condition monitoring; Data acquisition; Data processing; Fault detection; Machine learning; Machinery condition monitoring; Maintenance costs; pitch system; Programmable logic controllers; relevance vector machine; Relevance vector machines; SCADA systems; Statistical analysis; Supervisory control and data acquisition; System failures; Turbines; Wind power; wind turbine; Wind turbines
• ISSN: 1949-3029; 1949-3037
• DOI: 10.1109/TSTE.2019.2954834

Condition monitoring and early fault detection of wind turbine faults can reduce maintenance costs and prevent cascaded failures. This article proposes a new Normal Behavior Modeling (NBM) method to predict wind turbine electric pitch system failures using supervisory control and data acquisition (SCADA) information. The proposed method is particularly effective for online monitoring applications at a reasonable computational complexity. Briefly, in the data preprocessing stage of the proposed method, in order to remove interferential information and improve data quality, the operational state codes from turbine programmable logic controller are applied to filter SCADA data. In the modeling process, we designed a NBM method using optimized relevance vector machine (RVM) regression, which is relatively fast and computationally efficient. An adaptive threshold by the probabilistic output of RVM is proposed and used as the rule of anomaly detection. One normal case and three typical fault cases have been studied to demonstrate the feasibility of the proposed method. The performance of the method is assessed using 38 actual pitch system faults compared with two existing methods.