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.

There is provided a method of detecting lightning strikes in a wind turbine rotor blade. The wind turbine rotor blade has a lightning protection system. A digital camera or an optical-digital heat sensor is provided in the region of a rotor blade root, in a hub of the wind turbine or in or on a tower of the wind turbine in such a way that the digital camera at least partially optically detects a part of the lightning protection system. The part of the lightning protection system is optically detected by the camera to carry out optical temperature detection. An increase in temperature of the part of the lightning protection system is detected based on the optical detection by the camera.

A method is provided for monitoring an operational parameter of a wind turbine. The method comprising defining a peer limit, measuring the operational parameter during operation of the wind turbine; and comparing the measured operational parameter to the peer limit. The wind turbine is a member of a peer group of wind turbines, each wind turbine of the peer group comprising a common characteristic. The peer limit is defined using measurements of the operational parameter measured on the wind turbines of the peer group of wind turbines.

A method of estimating a temperature of a component of a wind turbine is provided, comprising, during a calibration period, receiving measurements of the temperature of the component and of one or more corresponding operational parameter; and calculating, using the measurements coefficients of a model of the temperature of the component. The model relates temperature at a current time to a temperature a preceding time, T.sub.n−1. The model is segregated into separate bins based on wind speed or power generated by the wind turbine. The method further comprises using the model to estimate a temperature of the wind turbine.

The present invention relates to a self-powered remote control system for a smart valve, the system comprising: a smart valve for regulating the flow of a fluid in a pipe; a sensing module for sensing the flow rate, pressure, and temperature of the fluid in the pipe; a power generation module for generating power according to the flow of the fluid; a control module for controlling the lifting or lowering of the opening/closing plate of the smart valve according to the flow rate, pressure, or temperature state sensed by the sensing module; and an administrator terminal for transmitting and receiving control signals to and from the control module, wherein the power generation module comprises: a conical fluid guide member provided in a direction in which the fluid is supplied; and a rotating member rotated by the fluid guided through the fluid guide member, whereby the operation of the smart valve can be controlled by manipulating the administrator terminal at a remote location, so as to supply the fluid into the pipe or intercept the supply of the fluid into the pipe.

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.

An inspection method according to the present disclosure includes a step of mounting an ultrasonic probe, a step of mounting a pulser receiver, a step of causing the ultrasonic probe to transmit ultrasonic waves, a step of causing the ultrasonic probe to receive a reflected wave of the ultrasonic waves reflected by the wind turbine blade, a step of causing the pulser receiver to acquire reflected-wave data, a step of causing the pulser receiver to wirelessly transmit the reflected-wave data, a step of causing at least one of antennas to receive the wirelessly transmitted reflected-wave data, and a step of causing an information processing device electrically connected to the at least two antennas to perform information processing on the reflected-wave data.

A deep learning-based cooling system temperature prediction apparatus has an artificial neural network modeled by connecting a plurality of artificial neural network submodels each including an input layer, a hidden layer, and an output layer is used. A pump flow speed, a cooling water flow rate, a battery inlet cooling water temperature, a motor inlet cooling water temperature, a radiator outlet cooling water temperature, a battery temperature, and a motor temperature are predicted by inputting at least one of a predetermined control variable, an environment variable, or a time variable to the plurality of artificial neural network submodels in accordance with a physical causality. A number of the plurality of artificial neural network submodels and the control variables or environment variables that are sequentially input to each submodel depend on divisional control and integral control of the cooling system.

A method for quickly predicting a fatigue life of a wrinkle defect-containing main spar in a wind turbine blade is provided. The method includes: S1: testing a tensile property of a wrinkle defect-containing main spar to be tested; S2: calculating, according to surface temperature data of the specimen obtained in step S1, intrinsic dissipated energy of the main spar specimen under different loading stresses; S3: plotting a relational graph between intrinsic dissipated energy of the specimen and a corresponding ultimate tensile strength (UTS) level; S4: establishing, based on a change of the intrinsic dissipated energy in a fatigue process, a normalized residual stiffness model containing parameters to be determined, and putting fatigue test data into the model; S5: deducing a fatigue life prediction model for the wrinkle defect-containing main spar specimen according to the normalized residual stiffness model with determined parameters; and S6: obtaining a normalized failure stiffness.

A method for computer-implemented determination of control parameters of a turbine in case of a component malfunction is provided. The method considers the impact of individual turbine characteristic values on the turbine performance in a turbine model in order to determine control parameters for the turbine without damaging it. The following includes the steps of: receiving, by an interface, an information indicating a component malfunction; identifying, by a processing unit, as to what power level the turbine is operated at, by a simulation of the operation of the turbine, the simulation being made with a given turbine model in which the identified component is set to be operated with a reduced function and in which one or more characteristic values characteristic values of the wind turbine are used as input parameter; and deriving, by the processing unit, the control parameters for the wind turbine from the identified power level.