  Data-driven methods based on Supervisory Control and Data Acquisition (SCADA)
become a recent trend for wind turbine condition monitoring. However, SCADA
data are known to be of low quality due to low sampling frequency and complex
turbine working dynamics. In this work, we focus on the phase I analysis of
SCADA data to better understand turbines' operating status. As one of the most
important characterization, the power curve is used as a benchmark to represent
normal performance. A powerful distribution-free control chart is applied after
the power generation is adjusted by an accurate power curve model, which
explicitly takes into account the known factors that can affect turbines'
performance. Informative out-of-control segments have been revealed in real
field case studies. This phase I analysis can help improve wind turbine's
monitoring, reliability, and maintenance for a smarter wind energy system.
