IEEE To improve the reliability of wind turbines, various condition monitoring systems (CMSs) have been developed and most of them transmit data using wired communication channels. Recently, wireless sensor networks (WSNs) have been used to transmit data in wind turbine CMS due to the low cost and easy deployment feature of WSNs. However, since wind turbines are installed in harsh environments, the sensors and sensor nodes used in the WSN-based wind turbines CMSs are easily subject to faults, leading to corruption of the signals used for condition monitoring, which decreases the reliability of the CMS. This paper proposes a three-stage method for detection and isolation of three most common sensor faults, i.e., SHORT fault, CONSTANT fault, and NOISE fault, in WSN-based wind turbine CMS. The proposed sensor fault detection and isolation (SFDI) greatly increases the accuracy and reliability of wind turbine CMSs. Data collected from wind turbines in the field are used to validate the effectiveness of the proposed method.
Pandit, R. K., & Infield, D. (2018). SCADA-based wind turbine anomaly detection using Gaussian process models for wind turbine condition monitoring purposes. IET Renewable Power Generation, 12(11), 1249–1255. https://doi.org/10.1049/iet-rpg.2018.0156