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Wind turbines often work under complex conditions which result in performancedegradation. Accurate performance degradation monitoring is essential to ensurethe reliable operation of wind turbines and reduce the maintenance costs. Windturbine power curve monitoring is an effective way to detect performancedegradation. However, due to the intermittency and fluctuation of wind speed,the wind speed range varies at different time periods, making power curvesdifficult to compare. Motivated by this, we proposed copula-based variationalinference framework and used it to establish a sequential incomplete wind powercurve estimation algorithm. First, a monotone power curve is constructed basedon 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 KLdivergencefactor is constructed to monitor wind turbine performance degradation. The realdata of a wind farm in the east of the United Kingdom shows that the proposedmethod can both improve the accuracy of wind turbine power curve modeling andmonitor wind turbine performance degradation more precisely and comprehensivelythan the existing approaches.

A Directional Monitoring Approach of Sequential Incomplete Wind Power Curves with Copula-based Variational Inference