  Wind turbines often work under complex conditions which result in performance
degradation. Accurate performance degradation monitoring is essential to ensure
the reliable operation of wind turbines and reduce the maintenance costs. Wind
turbine power curve monitoring is an effective way to detect performance
degradation. However, due to the intermittency and fluctuation of wind speed,
the wind speed range varies at different time periods, making power curves
difficult to compare. Motivated by this, we proposed copula-based variational
inference framework and used it to establish a sequential incomplete wind power
curve estimation algorithm. First, a monotone power curve is constructed based
on copula-based variational inference and integrated spline regression model.
Besides, the prior distribution of model parameters are sequentially updated.
Then, a directional control chart based on a new statistic named KLdivergence
factor is constructed to monitor wind turbine performance degradation. The real
data of a wind farm in the east of the United Kingdom shows that the proposed
method can both improve the accuracy of wind turbine power curve modeling and
monitor wind turbine performance degradation more precisely and comprehensively
than the existing approaches.

Applications