59results about "Neural network algorithms" patented technology

The invention relates to the technical field of wind turbine generator data analysis, and discloses an offshore wind turbine generator operation data analysis and prediction system. The system comprises a marine environment data integration module for collecting data to generate a multi-source time-space synchronization data set; the multi-modal feature fusion module is used for extracting cross-modal correlation features to generate a high-dimensional fusion feature tensor; the dynamic fault prediction module is used for constructing a two-way gating circulation network model to predict the degradation probability and the residual life of key components of the equipment; and the self-adaptive optimization control module is used for constructing a multi-target dynamic programming model to optimize a fan operation strategy. In addition, the system is further provided with a feedback correction module for correcting prediction model parameters, and a virtual sensor module based on a physical information neural network is used for monitoring tower stress and diagnosing sensor faults. According to the system, comprehensive monitoring, accurate fault prediction and optimal control of the offshore wind turbine generator are realized, the operation efficiency, reliability and safety of the wind turbine generator are effectively improved, and the operation and maintenance cost is reduced.

A method, system, and device for wind speed prediction and layout optimization in wind power generation are provided. The method includes: obtaining a basic wind resource dataset of a target region; constructing a physics-informed neural network model based on the basic wind resource dataset; obtaining wind speeds data at a specific location in a velocity field based on the physics-informed neural networks and constructing a training dataset; training the physics-informed neural network model based on the training dataset; reconstructing a wind speed distribution within the velocity field and predicting wind speeds for a next time period with a wind farm using the trained physics-informed neural network model; and optimizing a layout of a wind turbine cluster based on a reconstructed wind speed distribution within the velocity field. The present application reconstructs a two-dimensional velocity field of the wind farm by training the PINN and enables accurate ultra-short-term wind speed prediction.

The invention discloses a wind power generation system with a self-adaptive compensation function. The wind power generation system comprises a wind power generator, an energy storage device, a self-adaptive compensation controller and a power grid connection module, the self-adaptive compensation controller comprises a real-time monitoring module, a prediction analysis module, a self-adaptive compensation decision module and a control execution module, and the real-time monitoring module obtains operation parameters of the wind power generator and state parameters of the energy storage device; the predictive analysis module predicts and analyzes the wind power change trend and the power grid load demand, the self-adaptive compensation decision module generates control instructions of the wind driven generator and the energy storage device, the control execution module adjusts the operation state of the wind driven generator, and meanwhile the charging and discharging process of the energy storage device is controlled. By monitoring the states of the wind power and the power grid in real time, applying an intelligent algorithm to predict and analyze, and combining with an energy storage technology, self-adaptive compensation of wind power generation fluctuation is realized, the stability and reliability of wind power generation are improved, and adverse effects on the power grid are reduced.

The invention relates to the technical field of wind power management systems, and discloses an intelligent land wind power energy conservation and emission reduction management method and system. Comprising a data perception and acquisition module, a data transmission layer, a data processing and analysis layer, a power generation efficiency optimization module, an equipment health management module, a power grid coordination and energy storage optimization module, a carbon footprint tracking and reporting module and an application layer. The data sensing and collecting module collects wind speed, wind direction, temperature and air pressure data and generator rotating speed, gearbox temperature, blade angle and vibration frequency data in real time through a high-precision sensor, and low-delay data preprocessing is achieved through an edge calculation unit. In the invention, aiming at the delay problem of traditional yaw and variable pitch control, the system utilizes an intelligent algorithm to realize dynamic yaw and variable pitch control, and by accurately predicting the wind direction change and adjusting the fan direction and the blade angle in advance, the influence caused by complex wind conditions is effectively overcome, and the wind energy capture efficiency is greatly improved.

The invention belongs to the technical field of wind power control, and discloses a wind driven generator fixed time adaptive neural network variable pitch control method, which comprises the following steps: step 1, establishing a kinetic model of a wind driven generator, and initializing a system state and control parameters; 2, aiming at the wind driven generator system with external interference, designing a fixed time sliding mode surface based on a sliding mode thought; and step 3, designing a fixed-time adaptive neural network variable pitch controller. The method has the advantages that under the conditions that external interference exists in the wind driven generator and parameters are uncertain, rapid convergence of the rotor rotating speed tracking error within fixed time is achieved, and the convergence time does not depend on the initial state.

A method for determining operating states of a wind turbine (2) comprises providing an AI model (10), wherein the AI ​​model 10 is trainable to determine at least one current and / or future operating state of the wind turbine (2) on the basis of input data containing current and / or future environmental parameters (11a) and / or control data (12a), and determining the at least one current and / or future operating state (13) of the wind turbine (2) using the AI ​​model (10) on the basis of the input environmental parameters (11a) and / or control data (12a).

The application relates to the technical field of wind turbine data analysis, and discloses a system for analyzing and predicting offshore wind turbine operation data. The system comprises a marine environment data integration module, which collects data to generate a multi-source spatiotemporal synchronous data set; a multi-modal feature fusion module, which extracts cross-modal correlation features to generate a high-dimensional fusion feature tensor; a dynamic fault prediction module, which constructs a bidirectional gated recurrent network model to predict the degradation probability and residual life of key components of equipment; and a self-adaptive optimization control module, which constructs a multi-objective dynamic programming model to optimize the operation strategy of the wind turbine. In addition, the system is also provided with a feedback correction module to correct the parameters of the prediction model, and a virtual sensor module based on a physical information neural network to monitor the tower stress and diagnose sensor faults. The system realizes comprehensive monitoring, accurate fault prediction and optimized control of offshore wind turbines, effectively improves the operation efficiency, reliability and safety of the wind turbines, and reduces the operation and maintenance cost.

A method for monitoring a rotor assembly of a wind turbine includes receiving, via an imaging analytics module of a controller, thermal imaging data of the rotor assembly. The thermal imaging data includes a plurality of image frames. The method also includes automatically identifying, via a first machine learning model of the imaging analytics module, a plurality of sections of a rotor blade of the rotor assembly within the plurality of image frames until all sections of the rotor blade are identified. Further, the method includes selecting, via a function of the imaging analytics module, a subset of image frames from the plurality of image frames, the subset of image frames comprising a minimum number of the plurality of image frames required to represent all sections of the rotor blade. Moreover, the method includes generating, via a visualization module of the controller, an image of the rotor assembly using the subset of image frames.

The present disclosure provides a power generation control system for a self-powered water meter, a power generation control method for a self-powered water meter, a computer-readable storage medium, and an electronic device, relating to the technical field of power generation. The power generation control system for the self-powered water meter includes: a data processing module, configured to predict the load power consumption within a future target time period according to the historical power consumption data of a plurality of electrical loads, and determine the target guide vane opening of the water turbine based on the predicted load power consumption, the current load power consumption, and the user water consumption habit of the self-powered water meter; an opening comparison unit, configured to determine the opening difference between the reference guide vane opening and the target guide vane opening; a data conversion unit, configured to obtain the opening difference and convert the opening difference into a mechanical control signal, and the mechanical control signal is used to instruct the guide vane opening of the water turbine to be adjusted to the target guide vane opening. The present disclosure can achieve accurate control of power generation of the self-powered water meter.

A method of correcting turbine underperformance includes calculating a power production curve using monitored data, detecting changes between the monitored data and a baseline power production curve, generating operability curves for paired operational variables from the monitored data, detecting changes between the operability curves and corresponding baseline operability curves, comparing the changes to a respective predetermined metric, and if the change exceeds the metric, providing feedback to a turbine control system identifying at least one of the paired operational variables for each paired variable in excess of the metric. A system and a non-transitory computer-readable medium are also disclosed.

A wave energy capture system can include a plurality of arm assemblies. Each arm assembly includes a floatation device and an arm that is coupled to the floatation device and to a body via a pivot. The arm assemblies independently pivot around the pivots with respect to the body in response to movement of the floatation devices caused by waves in water. A mechanical energy capture system converts the independent pivoting of the plurality of arm assemblies around the pivots to electrical energy and provides the electrical energy to either an electronic device that is electrically coupled to the mechanical energy capture system to power the electronic device, or to a battery to recharge the battery.

A method includes determining a power curve image that includes a plurality of pixels that represents power production by a plurality of wind turbines of a wind farm as a function of wind speed. The method also includes determining, by a machine learning (ML) encoder model, a latent representation of attributes of the wind farm based on processing the power curve image by the ML encoder model. The method additionally includes obtaining an expected weather data corresponding to a future time. The method further includes determining, based on the latent representation and the expected weather data, an expected power production by the wind farm at the future time, and generating an output that includes the expected power production.

The invention provides a wind turbine method that includes receiving alarm state data indicating that the wind turbine has entered an alarm state in which operation of the wind turbine has stopped, and receiving sensor data from a plurality of sensors of the wind turbine indicative of operating conditions associated with the wind turbine. When the alarm state data is received, the method includes executing a trained machine learning model based on the received sensor data and the alarm state to obtain an output, where the machine learning model is trained based on historical data associated with a plurality of wind turbines, the historical data being indicative of the plurality of wind turbines previously being in the alarm state. The method includes providing, based on the obtained output, an action to be performed to allow the wind turbine to resume operation.

It is described a method of predicting a frequency value of a utility grid (3) to which a wind park (1) is connected, the method comprising: obtaining plural utility grid measurement values (7) pertaining to a predetermined time range before and until a present point in time; obtaining plural wind park measurement values (12) pertaining to the time range; feeding the plural utility grid measurement values (7) and the plural wind park measurement values (12) into a recurrent neural network trained to output the frequency value at at least one next point in time, the next point in time being in particular between 0.5 s and 2 s after the present point in time.

The invention discloses a digital intelligent simulation method for a hydroelectric generating set speed regulating system based on hydroelectric-mechanical coupling, and aims to solve the problems of low hydroelectric-mechanical coupling degree, poor controllability of a simulation tool and insufficient consideration of a water hammer effect in traditional simulation. The method comprises the following steps: establishing a hydraulic electromechanical coupling model containing hydraulic power (simulating hydraulic transient by a characteristic line method), machinery (a T-S fuzzy model self-defined speed regulator) and electricity (PSS / E tidal current + generator equation); constructing a load flow calculation interface layer for secondary development of a PSS / E Fortran API, and integrating intelligent algorithm modules of GSA, DEPSO and BP neural networks; carrying out real-time interactive simulation and optimizing PID parameters of the speed regulator; and verifying dynamic characteristics of the model based on a water hammer effect analytic solution. The method realizes hydroelectric-mechanical-electrical deep coupling, improves simulation controllability and precision, adapts to unit debugging and power grid dispatching verification, and ensures safe and stable operation of the hydroelectric generating set and the power grid.

The application provides a neural network-based yaw control method and device for a wind turbine, and relates to the technical field of wind turbine yaw control. The method comprises the following steps: obtaining state parameters of a target wind turbine; taking the state parameters of the target wind turbine as input, outputting a first predicted yaw angle of the wind turbine through a first yaw angle prediction model, and outputting a second predicted yaw angle of the wind turbine through a second yaw angle prediction model, wherein the first yaw angle prediction model and the second yaw angle prediction model are obtained by training different deep learning algorithms through historical state parameters and corresponding yaw angles of different wind turbines; determining a predicted yaw angle difference between the first predicted yaw angle and the second predicted yaw angle, and determining a target predicted yaw angle of the target wind turbine based on the first predicted yaw angle and the second predicted yaw angle according to the predicted yaw angle difference. The application improves the accuracy of yaw control for the wind turbine, and improves the efficiency and reliability of wind power generation.

Individual pitch control method and associated wind turbine controller The individual pitch control method (100) comprises an online phase and an offline phase. The online phase comprises acquiring (130) a current value of a parameter corresponding to the wind speed, selecting (140) a current control law and generating (150) a control signal based on the current control law and a current azimuth angle of the blade. The offline phase (110) comprises obtaining a trained reinforcement learning model. Selecting a control law in the online phase consisting in running in inference the trained reinforcement learning model to estimate the current control law from the current value of at least one parameter corresponding to the wind speed.

The present invention discloses a fan fault emergency prediction control method based on a data-driven strategy, which relates to the field of intelligent fan control algorithms. The present invention collects internal state data and external environment data of the fan in normal and emergency states through a historical data set, and uses these data to construct and train a deep learning model, so as to achieve accurate prediction and intelligent control of the fan state. Moreover, the intelligent control method proposed by the present invention can not only improve the operating efficiency of the fan, reduce the maintenance cost, but also significantly enhance the safety and reliability of the system.

A wake evaluation apparatus includes: a wake region detection unit 13 that analyzes an SAR image observed using a satellite's synthetic aperture radar using a machine learning model and detects one or more wake regions included in the SAR image; a wake position specifying unit 14 that specifies the position information of the wake region detected by the wake region detection unit 13 based on geographic information of the observation area measured by the satellite; and a wake region association unit 15 that associates the wake region with a wind power plant at a corresponding location based on the specified position information of the wake region and position information of the wind power plant specified in advance, and is configured to be able to evaluate wakes generated around a wind power plant without providing a transect by using a novel analysis method that is executed by applying the SAR image to the machine learning model.

Correcting measured wind characteristic of a wind turbine It is described a method of correcting a measurement value (101) of least one wind characteristic, in particular wind speed and / or wind direction, related to a wind turbine (1) having a rotor (3) with plural rotor blades (5) at least one having an adaptable flow regulating device (7) installed, the method comprising: measuring a value (101) of the wind characteristic; obtaining state information (107) of the adaptable flow regulating device (7); and determining a corrected value (111) of the wind characteristic based on the measured value (101) of the wind characteristic and the state information (107) of the adaptable flow regulating device (7).

A method for determining operating states of a wind turbine (2) comprises providing an AI model (10), wherein the AI ​​model 10 is trainable to determine at least one current and / or future operating state of the wind turbine (2) on the basis of input data containing current and / or future environmental parameters (11a) and / or control data (12a), and determining the at least one current and / or future operating state (13) of the wind turbine (2) using the AI ​​model (10) on the basis of the input environmental parameters (11a) and / or control data (12a).

The invention relates to a method for determining a spatial position of a portion of an infrastructure element (10), especially a wind energy plant (12). The method comprises obtaining first data (D1) indicative of an operational parameter of the infrastructure element (10) and / or indicative of an environmental parameter of the environment in which the infrastructure element (10) is located. The method further comprises inferring second data (D2) indicative of a position of the portion of the infrastructure element (10) based on the obtained first data (D1) and based on a trained data model (66), wherein the data model (66) is trained to provide a spatial position of the portion of an infrastructure element (10) based on the first data (D1). Moreover, the invention is directed to a method for monitoring the infrastructure element (10), to a method for training the data model (66) as well as to a data processing apparatus (30), a computer program and a computer readable storage medium for executing said methods. Furthermore, a system (28) for monitoring an infrastructure element (10) and an infrastructure element (10) are described.

The present invention discloses an intelligent control system, method, medium and equipment for a wind turbine generator set. The system includes: a wind measurement module for acquiring wind speed and wind direction data and transmitting the data to a main control module; an operation status monitoring module for recording the operation status data of the wind turbine generator set and transmitting the operation status data to the main control module; the main control module learns the input operation status data through a neural network algorithm and a deep learning model, calculates optimal control parameters in combination with the wind speed and wind direction data and preset design data, controls the wind turbine generator set to perform independent pitch control actions according to the optimal control parameters, and adjusts the stall state and clearance state of the wind turbine generator set during operation; the present invention uses the operation status data of the wind turbine generator set as input, performs learning and judgment through a neural network algorithm and a deep learning model, changes the control parameters of the wind turbine generator set during operation, does not require the addition of additional sensors, and improves the economy of the entire machine.

Provided is a method for computer-implemented monitoring of a component of a wind turbine, having access to a trained machine learning model which has been trained for one or more components of the same type of wind turbines. The trained machine learning model is configured to provide an output referring to a predetermined fault occurring at a component of a wind turbine by processing vibration signals in a predetermined domain which are measured in the vicinity of the component during the operation of the wind turbine. Vibration signals are mapped to corresponding vibration signals valid for the component based on one or more given kinematic parameters of the component and one or more given kinematic parameters of another component. The machine learning model is applied to the vibration signals valid for the component, resulting in an output referring to the predetermined fault occurring at the another component.

Disclosed is a method, performed by an electronic device, for controlling operation of a wind turbine. The method comprises obtaining wind turbine data associated with the wind turbine. The wind turbine data is indicative of conditions of operation of the wind turbine. The method comprises predicting an Amplitude Modulation, AM, noise parameter indicative of AM noise within a region at a distance from the wind turbine by applying a machine learning model to the wind turbine data. The method comprises generating, based on the AM noise parameter, control data indicative of a control operation of the wind turbine. The method comprises providing the control data to a controller for controlling the wind turbine in accordance with the control data.

The application provides a wind turbine variable pitch control method and device, a storage medium and a terminal device, relates to the technical field of wind turbine variable pitch control, and the method comprises the following steps: obtaining current wind resource information of an environment where a target wind turbine is located and current operating parameters of the target wind turbine; taking the wind resource information as input, outputting wind resource prediction information of the environment where the target wind turbine is located at a next unit time through a preset wind resource prediction model; determining a variable pitch fatigue state of the target wind turbine according to the operating parameters of the target wind turbine, and controlling the target wind turbine to perform a variable pitch operation at a first pitch angle at the next unit time or controlling the target wind turbine to perform a variable pitch operation at a second pitch angle at the next unit time based on the variable pitch fatigue state of the target wind turbine. The application can effectively improve the service life of the blade and the variable pitch bearing.

A method of operating a chiller to avoid future surge events, the method comprises applying chiller operating data associated with a chiller as an input to one or more machine learning models; and generating a threshold for a controllable chiller variable to prevent a future chiller surge event from occurring based on an output of the one or more machine learning models, further comprising affecting operation of the chiller based on the threshold to prevent the future chiller surge event from occurring. The method enables automatic control of a chiller to avoid future chiller surge events.

A wave energy capture system deployed in water converts mechanical motion induced by waves in the water to electrical energy. A controller of the wave energy capture system receives input regarding real-time wave conditions in a vicinity of the wave energy capture system. The controller applies a control model to the received input to select a value of a control parameter for the wave energy capture system, where the control model includes a model that has been trained using machine learning to take wave condition data as input and to output control parameter values selected based on the wave condition data in order to increase an amount of energy captured by the wave energy capture system. The controller implements the selected value of the control parameter on the wave energy capture system.

A method of predicting a frequency value of a utility grid to which a wind park is connected is provided, the method including: obtaining plural utility grid measurement values pertaining to a predetermined time range before and until a present point in time; obtaining plural wind park measurement values pertaining to the time range; feeding the plural utility grid measurement values and the plural wind park measurement values into a recurrent neural network trained to output the frequency value at at least one next point in time, the next point in time being in particular between 0.5 s and 2 s after the present point in time.

The invention relates to a method for determining a spatial position of a portion of an infrastructure element (10), especially a wind energy plant (12). The method comprises obtaining first data (D1) indicative of an operational parameter of the infrastructure element (10) and / or indicative of an environmental parameter of the environment in which the infrastructure element (10) is located. The method further comprises inferring second data (D2) indicative of a position of the portion of the infrastructure element (10) based on the obtained first data (D1) and based on a trained data model (66), wherein the data model (66) is trained to provide a spatial position of the portion of an infrastructure element (10) based on the first data (D1). Moreover, the invention is directed to a method for monitoring the infrastructure element (10), to a method for training the data model (66) as well as to a data processing apparatus (30), a computer program and a computer readable storage medium for executing said methods. Furthermore, a system (28) for monitoring an infrastructure element (10) and an infrastructure element (10) are described.