  The operation and maintenance costs of wind parks make up a major fraction of
a park's overall lifetime costs. They also include opportunity costs of lost
revenue from avoidable power generation underperformance. We present a
machine-learning based decision support method that minimizes these opportunity
costs. By analyzing the stream of telemetry sensor data from the turbine
operation, estimating highly accurate power reference relations and
benchmarking, we can detect performance-related operational faults in a
turbine- and site-specific manner. The most accurate power reference model is
selected based on combinations of machine learning algorithms and regressor
sets. Operating personal can be alerted if a normal operating state boundary is
exceeded. We demonstrate the performance fault detection method in a case study
for a commercial grid-connected onshore wind turbine. Diagnosing a detected
underperformance event, we find that the observed power generation deficiencies
coincide with rotor blade misalignment related to low hydraulic pressure of the
turbine's blade actuators.
