Abstract: While the wind power system deployed in offshore with Wind Turbines (WT) have been considered as a significant sustainable energy source, its adverse operation conditions in offshore inevitably led operators to adopt a remote control management such as Wireless Sensor Network (WSN) technology for a reliable Condition Monitoring System (CMS). In general, the CMS with WSN has been considered as an efficient technique to improve WT availability and reduce the operation and maintenance costs. However, it still need a highly reliable monitoring and diagnosis technique to prevent the CMS from falling into a fault decision. Thus in this study, we propose an efficient Fuzzy inference rule based fault diagnosis algorithm to analyze and diagnose the acquired sensing data from monitoring sensors in CMS with WSN structure. We apply two parameters, i.e., the spectrum correlation calculation parameter of vibration frequency and the deviation range calculation parameter of fault mode sensor data as inputs of Fuzzy membership functions to diagnose the failure condition of WT. The computer simulation results showed that the proposed algorithm could be useful to lower the error probability of fault decision of WT's condition.
Hyung-Whan Choi, Dong-Keun Jeon and Yeonwoo Lee, 2018. Fuzzy Inference Rule Based Fault Diagnosis Decision Algorithm for Wireless Sensor Network based Wind Turbine Power System. Journal of Engineering and Applied Sciences, 13: 8708-8711. Asian Journal of Information Technology, 18: 250-260.