Fault Detection for Wind Turbine System Using Fractional Extended Dispersion Entropy and Cumulative Sum Control Chart

Reliable and quick fault detection is crucial for the wind turbine system (WTS) to detect any potential abnormalities and early faults in time. However, constructing sensitive indicators to reflect the operation state of WTS is still a challenging work. In view of the conceptions of fractional calculus and dispersion entropy (DE), this article proposes a novel indicator called fractional extended DE (FrEDE) to cope with the challenge. Specifically, a connection block with an information factor is first applied to enhance the utilization of information in the raw signals. Moreover, fractional calculus is integrated into entropy calculation to support a constructive interplay in the fault detection of complex WTS. The experiments on simulated data investigate the effect of parameters on FrEDE and verify its ability to detect the dynamic changes of complex system. Finally, FrEDE is further applied to two real-world datasets. Comparative experimental results show that FrEDE is able to accurately differentiate normal between faulty states, and a detection scheme fusing FrEDE and cumulative sum control chart (CUSUM) is accessible to detect abnormal states and provide early failure alarm effectively.

Automated summary

The article proposes a novel indicator called FrEDE to cope with the challenges of fault detection in wind turbine systems. By utilizing the concepts of fractional calculus and dispersion entropy, and integrating information factor and entropy calculation, FrEDE is able to accurately detect the dynamic changes of complex systems and effectively differentiate between normal and faulty states.