A condition monitoring device for a wind turbine power generating apparatus provided with an auxiliary motor power supply system including a power-supply-side line connected to a power supply and a plurality of auxiliary-motor-side lines diverging from the power-supply-side line and connected to a plurality of auxiliary motors, respectively, comprises: a current measurement device for measuring a current flowing through the power-supply-side line; and a control device for controlling the plurality of auxiliary motors. The control device is configured to, when a generator of the wind turbine power generating apparatus is in a standby state where power generation is stopped at a low wind speed, execute a single sequential operation mode in which each of the plurality of auxiliary motors is singly and sequentially operated. The current measurement device is configured to measure a current flowing through the power-supply-side line during execution of the single sequential operation mode by the control device.

A system and method are provided for controlling a wind turbine. Accordingly, a controller of the wind turbine detects a loss of traction of the slip coupling based on a difference between data indicative of a rotor operating parameter and data indicative of a generator operating parameter. The controller then determines an angle of slip corresponding to the loss of traction as a function of the difference. Based, at least partially on the angle of slip, a degradation value for the slip coupling is determined. A control action is implemented based on the degradation value.

Disclosed are a method and apparatus for detecting a fault, and a method and apparatus for training a model. The method includes: acquiring characteristic data and actual temperature of a first wind turbine among n wind turbines, wherein the characteristic data of the first wind turbine is intended to characterize a working state of the first wind turbine, and n is an integer greater than 1; acquiring a prediction temperature set by inputting the characteristic data of the first wind turbine into a temperature prediction model corresponding to each of the n wind turbines; and detecting, based on the predicted temperature set and the actual temperature of the first wind turbine, whether the first wind turbine encounters a fault. Compared with the related art which depends on the working experience of the staff, the technical solution according to the embodiments of the present disclosure can more accurately detect whether a wind turbine encounters a fault, and provide early warning in time, so as to reduce the failure rate of the wind turbine.

A system and method are provided for controlling a wind turbine of a wind farm. Accordingly, a controller implements a first model to determine a modeled performance parameter for the first wind turbine. The modeled performance parameter is based, at least in part, on an operation of a designated grouping of wind turbines of the plurality of wind turbines, which is exclusive of the first wind turbine. The controller then determines a performance parameter differential for the first wind turbine at multiple sampling intervals. The performance parameter differential is indicative of a difference between the modeled performance parameter and a monitored performance parameter for the first wind turbine. A second model is implemented to determine a predicted performance parameter of the first wind turbine at each of a plurality of setpoint combinations based, at least in part, on the performance parameter differential the first wind turbine. A setpoint combination is then selected based on the predicted performance parameter and an operating state of the first wind turbine is changed based on the setpoint combination.

A wind turbine control unit includes an upsampling module that receives a first control signal that includes a current control sample value and a predicted control trajectory. The upsampling module also calculates a second control signal in dependence on the current control sample value and the predicted control trajectory. The second control signal has a higher frequency than the first control signal. The upsampling module further outputs the second control signal for controlling an actuator.

A system and method are provided for controlling a wind turbine of a wind farm. Accordingly, a controller implements a first model to determine a modeled performance parameter for the first wind turbine. The modeled performance parameter is based, at least in part, on an operation of a designated grouping of wind turbines of the plurality of wind turbines, which is exclusive of the first wind turbine. The controller then determines a performance parameter differential for the first wind turbine at multiple sampling intervals. The performance parameter differential is indicative of a difference between the modeled performance parameter and a monitored performance parameter for the first wind turbine. A second model is implemented to determine a predicted performance parameter of the first wind turbine at each of a plurality of setpoint combinations based, at least in part, on the performance parameter differential the first wind turbine. A setpoint combination is then selected based on the predicted performance parameter and an operating state of the first wind turbine is changed based on the setpoint combination.

Wind turbine lightning stroke diagnostic apparatus for a wind turbine having conductive blades. A lightning protection system input is provided for receiving measured current data associated with a current conducted by the lightning protection system following a lightning stroke. An air pressure sensor is mounted using a mount within an internal cavity of a conducive wind turbine blade assembly. A sensor monitor monitors the measured current data and the output of the air pressure sensor for identifying a bypass lightning stroke when a measured current increase coincides with the detection of a lightning generated shockwave within the internal cavity by the air pressure sensor.

Embodiments herein describe a system used to estimate the presence of water on a sensor. A parameter maintains a wind sensor temperature. The parameter can be tracked and evaluated to indicate a likelihood of water on the sensor. Alternatively, or in combination with the above, the sensor is adjusted intentionally or deactivated and reactivated to track a parameter response which is then used to indicate a likelihood of water on the sensor.

One embodiment provides a system for facilitating fault diagnosis. During operation, the system collects current signals associated with a physical object which comprises a rotating machine. The system demodulates the collected signals to obtain current envelope signals, which eliminates fundamental frequencies and retains fault-related frequencies. The system resamples the current envelope signals, which converts the fault-related frequencies to constant frequency components. The system enlarges, by a fault-amplifying convolution layer, the resampled envelope signals to obtain fault information. The system provides the fault information as input to a deep convolutional neural network (CNN). The system generates, by the deep CNN, an output which comprises the fault diagnosis for the physical object.

Disclosed are a method and apparatus for detecting a fault, and a method and apparatus for training a model. The method includes: acquiring characteristic data and actual temperature of a first wind turbine among n wind turbines, wherein the characteristic data of the first wind turbine is intended to characterize a working state of the first wind turbine, and n is an integer greater than 1; acquiring a prediction temperature set by inputting the characteristic data of the first wind turbine into a temperature prediction model corresponding to each of the n wind turbines; and detecting, based on the predicted temperature set and the actual temperature of the first wind turbine, whether the first wind turbine encounters a fault. Compared with the related art which depends on the working experience of the staff, the technical solution according to the embodiments of the present disclosure can more accurately detect whether a wind turbine encounters a fault, and provide early warning in time, so as to reduce the failure rate of the wind turbine.