The present invention provides a hydraulic turbine cavitation acoustic signal identification method based on big data machine learning. According to the method, time sequence clustering based on multiple operating conditions under the multi-output condition of the hydraulic turbine set is performed by utilizing an neural network, characteristic quantities of the hydraulic turbine set under a steady condition in a healthy state is screened; a random forest algorithm is introduced to perform feature screening of multiple measuring points under steady-state operation of the hydraulic turbine set, optimal feature measuring points and optimal feature subsets are extracted, finally a health state prediction model is constructed by using gated recurrent units; whether incipient cavitation is present in the equipment is judged. The present invention can effectively identify the occurrence of incipient cavitation in the hydraulic turbine set, reducing unnecessary shutdown of the equipment and prolonging the service life.

The system and method of the present disclosure is configured to monitor changes associated with an air gap by: (1) receiving one or more sensor signals from one or more sensors that are indicative of changes associated with the air gap; and (2) comparing the changes associated with the air gap to certain thresholds to determine if the air gap is in need of attention. The system includes at least one proximity sensor arranged adjacent to the air gap, to monitor the air gap, and a controller. The controller is configured to receive the sensor signal(s) indicative of the changes associated with the air gap. The controller also is configured to compare the changes associated with the air gap to one or more air gap thresholds, and to implement a control action based on this comparison.

A method for determining improved control parameters of a number of wind turbines of a wind park is provided. The method considers the impact of individual turbine manufacturing tolerances on the turbine performance, thereby avoiding under-utilization of those wind turbines. The method includes the steps of: receiving, by an interface, one or more actual manufacturing tolerances of characteristic values for each of the number of wind turbines; determining, by a processing unit, for each of the number of wind turbines a power versus wind speed map which is calculated from a given turbine model with the one or more actual manufacturing tolerances of the respective wind turbines as input parameters; and deriving, by the processing unit, the control parameters for each of the number of wind turbines from their associated power versus wind speed map.

A method of adjusting a drive mechanism includes measuring backlashes between the ring gear and the plurality of drive devices, and determining about positions of the plurality of drive devices with reference to the ring gear based on the backlashes measured in the measurement step. The measurement step includes: aligning the pinion of one of the plurality of drive devices to face a reference position in a circumferential direction of the ring gear and measuring a backlash between the ring gear and the said one drive device; and aligning the pinion of another one of the plurality of drive devices to face the reference position of the ring gear by revolving the plurality of the drive devices relative to the ring gear, and measuring a backlash between the ring gear and the said another drive device different from the said one drive device whose backlash has been measured.

Embodiments of the present disclosure include a retrofit auxiliary nacelle yaw position control system that enables advanced nacelle yaw position control of a wind turbine by comparing a desired nacelle yaw position signal with the actual nacelle yaw position and generating a virtual relative wind direction signal that is provided to the existing turbine control unit. This method and system enable implementation of wake steering, collective yaw optimization and dynamic yaw optimization of a collection of wind turbines referred to as a wind plant. Modification of the existing turbine control unit is not required, greatly simplifying the implementation process of advanced yaw control strategies on existing wind plants.

A method for computer-implemented maximization of annual energy production of several wind turbines of a wind park is provided. The method considers the impact of individual turbine manufacturing tolerances on the turbine performance, thereby avoiding under-utilization of those wind turbines. The method includes: receiving, by an interface, one or more actual manufacturing tolerances of characteristic values for each of the number of wind turbines; determining, by a processing unit, for each of the number of wind turbines a power versus wind speed map which is calculated from a given turbine model with the one or more actual manufacturing tolerances of the respective wind turbines as input parameters; determine, based on the power versus wind speed map of each of the number of wind turbines a respective performance measure; and assign a selected siting position for each wind turbine in the wind park according to its determined performance measure.

The present disclosure relates to a method for operating a wind power installation, to an associated wind power installation and to a wind farm. The method comprises the following steps: determining at least two, preferably at least three and particularly preferably all the environmental parameters of the environment of the wind power installation selected from the list consisting of: turbulence intensity, air density, air temperature and shear; providing boundary conditions for operating the wind power installation, the boundary conditions containing at least one from a load boundary condition, a noise level boundary condition and a power boundary condition; adapting an operational control, in particular an operating point and/or an operating characteristic, of the wind power installation on the basis of a combination of the changes in the determined environmental parameters taking into consideration the boundary conditions.

Embodiments of the present disclosure include a retrofit auxiliary nacelle yaw position control system that enables advanced nacelle yaw position control of a wind turbine by comparing a desired nacelle yaw position signal with the actual nacelle yaw position and generating a virtual relative wind direction signal that is provided to the existing turbine control unit. This method and system enable implementation of wake steering, collective yaw optimization and dynamic yaw optimization of a collection of wind turbines referred to as a wind plant. Modification of the existing turbine control unit is not required, greatly simplifying the implementation process of advanced yaw control strategies on existing wind plants.

The invention relates to a method for determining the wear of a sliding bearing (19) arranged in a wind turbine gearbox (7), in particular a planetary gearbox, which sliding bearing (19) serves for mounting a gearbox component (23). A radial displacement of the position of the gearbox component (23) pivoted by means of the sliding bearing (19) is detected by means of a distance sensor (25) arranged in the wind turbine gearbox (7).

Embodiments of the present disclosure include a retrofit auxiliary nacelle yaw position control system that enables advanced nacelle yaw position control of a wind turbine by comparing a desired nacelle yaw position signal with the actual nacelle yaw position and generating a virtual relative wind direction signal that is provided to the existing turbine control unit. This method and system enable implementation of wake steering, collective yaw optimization and dynamic yaw optimization of a collection of wind turbines referred to as a wind plant. Modification of the existing turbine control unit is not required, greatly simplifying the implementation process of advanced yaw control strategies on existing wind plants.