Blade imbalance fault diagnosis of doubly fed wind turbine based on current coordinate transformation

2018 ◽  
Vol 14 (2) ◽  
pp. 185-191 ◽  
Author(s):  
Dong Yang ◽  
Ju Tang ◽  
Fuping Zeng
Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3396 ◽  
Author(s):  
Mingzhu Tang ◽  
Wei Chen ◽  
Qi Zhao ◽  
Huawei Wu ◽  
Wen Long ◽  
...  

Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response.


2017 ◽  
Vol 10 (1) ◽  
pp. 56 ◽  
Author(s):  
Zakaria Sabiri ◽  
Nadia Machkour ◽  
Nabila Rabbah ◽  
Mohammed Nahid ◽  
Elm'kaddem Kheddioui

Sign in / Sign up

Export Citation Format

Share Document