Wind Motor Control TRIZ Cases

Feedforward yaw torque compensation uses aerodynamic load data to hold nacelle position, reducing backlash, noise, and yaw system wear.

Staggered wind turbine yawing and other high-load starts cut peak backup power demand without oversizing the backup system.

Rotor and generator parameter differences reveal slip coupling traction loss, enabling degradation-based control and proactive wind turbine maintenance.

Sensor signature comparison detects blade liberation and triggers rapid rotor deceleration with torque override and blade feathering.

Icing detection triggers de-rated rotor speed and blade heating to limit ice loads while keeping wind turbines generating power.

Real-time aerodynamic state estimation and predictive control improve wind turbine actuator response while reducing wear from conservative models.

Switches between low and high idling rotor speeds to balance aerodynamic damping, structural loads, and off-grid power needs.

Real-time IoT control adjusts gearbox ratio, generator torque, and rotor speed to stabilize offshore wind power under turbulent wind.

An eddy-current brake regulates VAWT shaft speed in strong winds, protecting the generator while reducing friction brake wear and downtime.

Azimuth-domain vibration analysis detects severe wind turbine rotor imbalance within a rotation and enables controlled pitch-based stopping.

Virtual meshing angle tracking uses shaft position and gearbox topology to avoid internal measurements while enabling noise-reducing torque modulation.

Aggregated turbine power offsets let a wind park suppress PCC oscillations while preserving local mechanical damping control.

Flow-noise sensors and an aerodynamically shaped anti-noise housing cut wind turbine blade noise without harming blade integrity or drag.

After nacelle stopping, timed brake release or higher hydraulic pressure redistributes residual yaw-drive loads and helps prevent overload.

Estimated wind speed from rotor speed, power, and pitch enables accurate subrated-to-rated switching while reducing stress and power loss.

Blade accelerometers and acoustic sensors detect dust storms, loose particles, and rotor anomalies early, enabling control action to limit erosion.

Iterative outlier removal cleans unhealthy asset states from training data, improving anomaly detection robustness without manual filtering.

Movable blade aerodynamic devices create asymmetric forces and turbulence to speed wake recovery and reduce downstream turbine losses.

A UAV-deployed line scrapes ice from wind turbine blades, cutting energy use and cost while limiting surface damage.

Sensor-based trigger signals detect excessive rotor blade torsion and adjust pitch, speed, or power to reduce damaging wind turbine loads.

Amplitude and phase analysis across load directions helps detect dominant rotor oscillation modes with fewer false alarms and less energy loss.

Leading-edge corrective mass shifts the blade center of mass forward to raise aerodynamic damping and suppress unstable aeroelastic modes.

Multi-level sawtooth trailing-edge teeth guide eddy currents to cut wind turbine blade noise, especially at high wind speeds.

Shape-changing spokes, gyroscopic stabilization, and magnetic bearings help this compact turbine capture fluid energy with less friction and wear.

Real-time yaw monitoring and fault modeling identify sensor, contactor, and motor faults early to cut downtime and improve wind alignment.

When one blade load sensor fails, estimated load signals and coordinate transforms keep wind turbine pitch control active and loads reduced.

Multi-directional load amplitude and phase analysis identifies dominant rotor oscillation modes and reduces unnecessary wind turbine shutdowns.