Digital twin intelligent perception system, method, medium and device applied to a wind turbine generator

Abstract

The application relates to a digital twin intelligent perception system, method, medium and equipment applied to a wind turbine, which comprises an end side, a station side and a center side; the end side comprises a data perception layer and a data acquisition calculation layer; the data perception layer comprises various sensing devices; the station side comprises a first area data management module, a second area data management module and a third area data management application module; and the center side comprises a digital twin simulation model construction module, a virtual perception module and an intelligent diagnosis module. Through the application, multi-source data standardization fusion and data hierarchical storage can be realized, and virtual perception, intelligent diagnosis and early warning and digital twin simulation model functions can be developed and deployed at the center side. The application can realize multi-source data standardization fusion and data hierarchical storage, and can develop and deploy virtual perception, intelligent diagnosis and early warning and digital twin simulation model functions at the center side, thereby effectively solving the problems of great difficulty in obtaining offshore wind turbine data, low completeness and low accuracy.

Description

Technical Field

[0001] This invention relates to the field of wind turbines, and more specifically, to a digital twin intelligent sensing system, method, medium, and device for use in wind turbine generators. Background Technology

[0002] While installed capacity is rapidly increasing, the "software guarantee" of wind turbine operation monitoring is also receiving more attention. Due to the remote location and harsh environment of wind farms, the operation and maintenance costs of wind turbines account for a high proportion. With the rapid iteration of offshore wind power technology, measurement accuracy, communication speed, and computing power are constantly improving, and the volume and richness of data are growing exponentially. Communication speed and computing power are the guarantee of the real-time performance and accuracy of digital twins, while measurement accuracy, data volume, and data richness can support the high-fidelity, full-scale representation of digital twins. However, for building digital twin models of complex equipment like offshore wind turbines, measurement and sensing work still faces challenges such as difficulty in data acquisition, low data completeness, and low data accuracy. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a digital twin intelligent sensing system, method, medium and device for wind turbines, addressing the problems existing in the prior art.

[0004] The technical solution adopted by the present invention to solve its technical problem is: to construct a digital twin intelligent sensing system for wind turbines, including: end side, station side and center side;

[0005] The endpoint includes a data sensing layer and a data acquisition and calculation layer; the data sensing layer includes multiple sensing devices, which are used to acquire and monitor various types of sensing data; the data acquisition and calculation layer is used to acquire, process, calculate and store raw data, as well as acquire and process the various types of sensing data.

[0006] The station side includes: a zone 1 data management module, a zone 2 data management module, and a zone 3 data management application module; the zone 1 data management module is used to access and manage the sensing data; the zone 2 data management module is used to cache the sensing data and manage the sensing data through gates; the zone 3 data management application module is used to manage and apply the sensing data and the raw data.

[0007] The central side includes: a digital twin simulation model construction module, a virtual sensing module, and an intelligent diagnosis module; the digital twin simulation model construction module is used to construct a digital twin model of the wind turbine using a combination of finite element analysis and data correction; the virtual sensing module is used to access the various sensing data, real-time operating data, and equipment status data; the intelligent diagnosis module is used to construct an equipment diagnosis model based on the various sensing data, the real-time operating data, the equipment status data, and the equipment failure mechanism, and to perform fault early warning diagnosis and life prediction based on the equipment diagnosis model.

[0008] In the digital twin intelligent sensing system for wind turbines described in this invention, the various sensing devices include: a first load sensor installed on the inner wall of the wind turbine blade, a second load sensor installed on the tower section, a nacelle-type laser wind radar installed on the top of the nacelle, a vibration pickup installed on the tower section, a dynamic tilt angle monitoring sensor installed on the top of the tower, and a blade acoustic signature monitoring device installed on the surface of the wind turbine blade.

[0009] The data acquisition and computing layer includes: a standardized data acquisition module, an edge computing module, a data storage module, a controller, and a fieldbus for connecting the modules; the standardized data acquisition module is used to collect and process the various sensing data in a standardized manner; the data storage module is used to store data; the controller is used to control and manage the various modules; and the edge computing module is used to collect, calculate, and store the raw data.

[0010] In the digital twin intelligent sensing system for wind turbine generators described in this invention, the edge computing module includes:

[0011] An edge node device, wherein the edge node device is used to collect the raw data;

[0012] The data preprocessing module is used to filter, reduce noise, and remove outliers from the raw data, as well as to perform data format conversion and standardization.

[0013] A local storage module, wherein the local storage module is used for local storage of the original data;

[0014] An edge computing submodule is used to perform data analysis and computation based on the edge device.

[0015] The communication module utilizes network slicing technology to provide connectivity between the edge node device and the cloud or other devices.

[0016] The digital twin intelligent sensing system for wind turbines described in this invention further includes: a data management service unit applied to the end side, the site side, and the center side;

[0017] The data management service unit includes: an edge-side IoT layer;

[0018] The endpoint IoT layer includes:

[0019] A data processing module is used to process the structured data, semi-structured data, or unstructured data collected from the terminal side.

[0020] A data encoding model construction module, which is used to construct a standardized data encoding module;

[0021] The user permission configuration module is used to support users on the site side and the center side to configure permissions.

[0022] A data storage strategy configuration module is used to support data storage strategy configuration.

[0023] In the digital twin intelligent sensing system for wind turbine generators described in this invention, the edge-side IoT layer further includes:

[0024] The object model definition and mapping module is used to define and map the devices in the edge IoT layer in a custom digital representation and construct an object model based on the digital representation, and map the physical attributes and functions of the devices to the object model.

[0025] The object model version update module is used to incrementally update the object model based on a preset update strategy after detecting an object model version update.

[0026] The object model sharing and collaboration module is used to support the sharing and collaboration of object models;

[0027] A visualization module, which provides a graphical interface.

[0028] In the digital twin intelligent sensing system for wind turbine generators described in this invention, the digital twin simulation model construction module includes:

[0029] A wind speed model building module is used to build a model based on wind-related parameters to obtain a wind speed model.

[0030] A blade model construction module is used to perform finite element analysis modeling based on blade parameters to obtain a blade model.

[0031] A tower model construction module is used to perform finite element analysis and modeling of tower parameters to obtain a load model;

[0032] An electrical system model building module is used to perform model building based on equipment parameters to obtain an electrical system model and control strategy.

[0033] The simulation model construction module is used to construct a model based on the wind speed model, the blade model, the tower model, the electrical system model, and the control strategy to obtain a digital twin model of the wind turbine.

[0034] In the digital twin intelligent sensing system for wind turbine generators described in this invention, the intelligent diagnostic module includes:

[0035] An early warning model construction module is used to construct an early warning model based on an intelligent sensing model.

[0036] A diagnostic model building module is used to build a model based on the various sensing data, the real-time operating data, the equipment status data, and the equipment failure mechanism to obtain the equipment diagnostic model.

[0037] A fault early warning and diagnosis module is used to perform fault early warning diagnosis and life prediction based on the early warning model and / or the equipment diagnosis model.

[0038] This invention also provides a digital twin intelligent sensing method for wind turbine generators, applied to the aforementioned digital twin intelligent sensing system for wind turbine generators, comprising the following steps:

[0039] Acquire sensing data and device parameters from the edge;

[0040] Based on the sensing data, finite element analysis modeling is performed to obtain wind speed model, blade model and tower model;

[0041] Based on the equipment parameters, a model is created to obtain an electrical system model and control strategy.

[0042] By combining the wind speed model, the blade model, the tower model, the electrical system model, and the control strategy, a digital twin model of the wind turbine is obtained.

[0043] The digital twin model of the wind turbine is corrected to obtain the digital twin model of the target.

[0044] The digital twin model of the target is made compatible with the cloud platform for data exchange.

[0045] After data exchange, fault early warning diagnosis and life prediction are carried out based on the constructed equipment diagnostic model.

[0046] The present invention also provides a storage medium storing a computer program adapted for loading by a processor to execute the steps of the digital twin intelligent sensing method for wind turbines as described above.

[0047] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the digital twin intelligent sensing method applied to wind turbines as described above by calling the computer program stored in the memory.

[0048] The digital twin intelligent sensing system, method, medium, and equipment for wind turbines implemented in this invention have the following beneficial effects: It includes: an end-side, a site-side, and a central-side; the end-side includes: a data sensing layer and a data acquisition and computing layer; the data sensing layer includes various sensing devices; the site-side includes: a zone 1 data management module, a zone 2 data management module, and a zone 3 data management application module; the central-side includes: a digital twin simulation model construction module, a virtual sensing module, and an intelligent diagnostic module. This invention enables standardized fusion of multi-source data, hierarchical data storage, and the development and deployment of virtual sensing, intelligent diagnostic early warning, and digital twin simulation model functions on the central-side. This invention effectively solves the problems of high difficulty in acquiring, low completeness, and low accuracy of data from offshore wind turbines. Attached Figure Description

[0049] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0050] Figure 1 This is a system architecture block diagram of an embodiment of the digital twin intelligent sensing system for wind turbine generators provided by the present invention;

[0051] Figure 2 This is a flowchart illustrating an embodiment of the digital twin intelligent sensing method for wind turbine generators provided by the present invention.

[0052] Figure 3 This is a hardware structure block diagram of the electronic device provided by the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] In a preferred embodiment, such as Figure 1 As shown, the digital twin intelligent sensing system applied to wind turbines includes: end side, station side, and center side.

[0055] The edge side includes: a data sensing layer and a data acquisition and computing layer; the data sensing layer includes a variety of sensing devices, which are used to acquire and monitor various types of sensing data; the data acquisition and computing layer is used to collect, process, calculate and store raw data, as well as to collect and process various types of sensing data.

[0056] In some embodiments, the various sensing devices include: a first load sensor installed on the inner wall of the wind turbine blades, a second load sensor installed on the tower cross-section, a nacelle-type laser wind radar installed on the top of the nacelle, a vibration pickup installed on the tower cross-section, a dynamic tilt angle monitoring sensor installed on the top of the tower, and a blade acoustic signature monitoring device installed on the surface of the wind turbine blades. Optionally, in embodiments of the present invention, the sensing data includes, but is not limited to: blade root load monitoring data, blade acoustic signature monitoring data, tower load monitoring data, laser radar wind measurement data, tower tilt angle monitoring data, and whole-machine vibration mode monitoring data.

[0057] Specifically, blade root load monitoring data can be obtained through multiple first load sensors installed at appropriate locations at the root of the wind turbine blade. These first load sensors are fixed to the inner wall of the blade root. Optionally, the blade root load monitoring data includes, but is not limited to, data on parameters such as stress, strain, and bending moment in the blade flapping and swaying vibration directions. Blade acoustic signature monitoring data can be obtained through blade acoustic signature monitoring equipment (such as intelligent sound acquisition equipment suitable for wind turbines) installed on the surface of the wind turbine blade. This equipment can collect the sound signals of blade operation. Tower load monitoring data can be obtained through multiple second load sensors arranged at appropriate cross-sections of the tower. Each second load sensor at each cross-section must simultaneously monitor strain in at least two directions. LiDAR wind measurement data can be obtained through a nacelle-mounted LiDAR wind measurement radar installed on the top of the nacelle. This nacelle-mounted LiDAR wind measurement radar can monitor the real-time wind conditions in front of the offshore wind turbine. Tower tilt angle monitoring data can be obtained through a dynamic tilt angle monitoring sensor installed on the top of the tower. Dynamic tilt sensors monitor the tower's XY-axis tilt angle and XY-axis angular velocity relative to sea level. Vibration modal monitoring data for the entire unit can be obtained through vibration pickups (multiple of them) installed at appropriate cross-sections of the tower. The lower limit of the pickup's frequency band is no greater than 0.2Hz, and the entire system's sampling frequency is no less than 50Hz, employing synchronous sampling technology.

[0058] The data acquisition and computing layer includes: a standardized data acquisition module, an edge computing module, a data storage module, a controller, and a fieldbus for connecting the modules. The standardized data acquisition module is used for standardized acquisition and processing of various sensing data; the data storage module is used for data storage; the controller is used for control and management of the various modules; and the edge computing module is used for acquisition, calculation, and storage of raw data. This invention is based on advanced sensor technology, employs standardized data acquisition, and constructs a flexible and elastic intelligent sensing layer to realize the acquisition of sensing data and edge computing. For example, a relevant advanced sensing system can be deployed on five typical generator units, including two nacelle-type laser wind-measuring radars, and finally connected to each module via a fieldbus to achieve efficient data transmission and communication.

[0059] In some embodiments, the standardized data acquisition module can provide industry-standard data acquisition hardware devices, including sensor interface modules, data conversion units, etc.; it can support plug-and-play modular functional design, facilitating rapid access to different types of sensors and devices; it can support standardized acquisition and processing of various sensing data, including but not limited to: environmental data, device status data, image and video data, etc.; it can support various sensor interfaces (including analog input, digital input, communication interfaces such as RS-485, CAN, etc.); it provides standardized interface adapters to adapt to different types of sensors and data sources; it realizes real-time acquisition, conversion and preprocessing of sensor data; it supports real-time data acquisition requirements, including data sampling rate, data accuracy, etc.; it supports industry standard communication protocols (including Modbus TCP / RTU, OPC UA, Ethernet / IP, etc.), and supports wireless AP standardized communication protocols when necessary; it provides data upload and remote management functions, supports network configuration and data transmission; and it provides modular hardware components, including data acquisition modules, data processing modules, communication modules, etc.

[0060] The edge computing module includes: edge node devices for collecting raw data; a data preprocessing module for filtering, noise reduction, and outlier removal of the raw data, as well as data format conversion and standardization; monitoring and control capabilities, enabling real-time control operations based on preset algorithms or rules, and the ability to trigger alarms and control equipment start / stop; a local storage module for local storage of the raw data; an edge computing submodule for edge-side data analysis and computation; and a communication module that utilizes network slicing technology to provide connectivity between the edge node devices and the cloud or other devices. Specifically, the edge node devices are used to collect raw data. These edge node devices include, but are not limited to, routers, switches, gateways, smart sensors, cameras, industrial controllers, and servers. The data preprocessing module performs preliminary filtering, noise reduction, and outlier removal on the collected raw data to improve data quality; and performs data format conversion and standardization for subsequent transmission and processing. The local storage module temporarily stores the raw data locally to prevent data loss due to network failures or data transmission interruptions. The edge computing submodule can utilize data aggregation or trend analysis to perform edge-based data analysis and computation, reducing the computational burden on central or field-side servers. The communication module enables communication and networking, allowing communication with upper-layer systems or cloud platforms via wired (including Ethernet, serial port, fiber optic, etc.) or wireless (including WiFi, Bluetooth, cellular networks, etc.) methods to upload collected data; it supports multiple communication protocols (including Modbus, TCP / IP, MQTT, etc.) to ensure compatibility with different systems.

[0061] Furthermore, this edge computing module also includes: a model deployment and management module, a system self-diagnosis module, a security module, an authentication management module, and a remote management and configuration module. The model deployment and management module can be developed and deployed based on a standardized model development and deployment framework for the edge computing environment, possessing flexible scalability and configuration capabilities. The system self-diagnosis module enables system self-diagnosis and self-recovery, monitoring its own operational status, including hardware status and network connection status; in the event of a fault, it attempts automatic recovery or issues a fault alarm for timely maintenance. The security module encrypts collected data to ensure data security during transmission and storage. The authentication management module provides user authentication and access control functions to prevent unauthorized access and operation. The remote management and configuration module supports remote management and configuration of the edge computing controller, including parameter settings, software updates, and task scheduling. This edge computing module is also adaptable to harsh environments, possessing characteristics such as dustproof, waterproof, electromagnetic interference resistance, and salt spray resistance, enabling stable operation in harsh environments such as offshore wind power.

[0062] The station side includes: Zone 1 data management module, Zone 2 data management module, and Zone 3 data management application module; Zone 1 data management module is used to access and manage the sensing data; Zone 2 data management module is used to cache the sensing data and manage the sensing data through the gate; Zone 3 data management application module is used to manage and apply the sensing data and raw data.

[0063] Specifically, in some embodiments, the Zone 1 data management module is mainly used to access sensing data from different sources. Sensing data refers to data collected by various sensors (such as temperature sensors, humidity sensors, cameras, and sensors in the aforementioned sensing layer), which reflects the status information of the site's internal or surrounding environment. The Zone 1 data management module is responsible for ensuring that this data can be correctly received, preliminarily processed (such as format conversion, data cleaning, etc.), and prepared for further analysis or use.

[0064] The Zone 2 data management module includes a data caching module and a data gateway module. Since the sensed data may originate from multiple different devices and the data volume is large, a caching mechanism can be implemented to temporarily store this data to ensure system stability and response speed. This ensures that even if the main database or subsequent processing environment experiences a brief failure, important real-time data will not be lost. The data gateway module is used for the control mechanism in the process of transferring data from one security domain to another. Different application scenarios have different security requirements; the data gateway module is responsible for ensuring that data is transmitted according to established security policies and rules to prevent information leakage.

[0065] The three-zone data management application module includes: a data standardization module, a data storage module, a model management module, an inference platform, and a model computation module. Data from different sources often has different formats and standards. The data standardization module transforms this heterogeneous data into a unified standard format to facilitate subsequent data processing and analysis. The data storage module is responsible for long-term storage of processed data, potentially using various storage methods such as relational databases, NoSQL databases, or distributed file systems to meet different types of query needs and performance requirements. The model management module manages and maintains various models used for data analysis, including machine learning models and deep learning models. It supports model version control and training status tracking. The inference platform performs prediction or classification operations on newly input data based on existing models. The inference platform is a key component for achieving intelligent decision-making. The model computation module is responsible for executing model training tasks, adjusting model parameters based on historical data to improve model accuracy and efficiency. It may also support online learning, updating the model while continuously receiving new data.

[0066] The overall architecture design of the data center embodies a complete chain from data acquisition, preprocessing, storage to analysis and application, aiming to build an efficient, secure, and scalable data processing and application platform. Such a platform plays a crucial role in improving data center operational efficiency, optimizing resource allocation, and enhancing security.

[0067] The central component includes: a digital twin simulation model, a virtual sensing module, and an intelligent diagnostic module. The digital twin simulation model is used to construct a digital twin model of the wind turbine using a combination of finite element analysis and data correction. The virtual sensing module is used to access various sensing data, real-time operating data, and equipment status data. The intelligent diagnostic module is used to construct an equipment diagnostic model based on various sensing data, real-time operating data, equipment status data, and equipment failure mechanisms, and to perform fault early warning diagnosis and life prediction based on the equipment diagnostic model.

[0068] In some embodiments, the construction of the digital twin simulation model building module combines finite element analysis and data correction, and is built on a Matlab or Simulink platform. Optionally, the digital twin simulation model includes: a wind speed model building module, used to build a model based on wind-related parameters to obtain a wind speed model; a blade model building module, used to perform finite element analysis modeling based on blade parameters to obtain a blade model; a tower model building module, used to perform finite element analysis modeling based on tower parameters to obtain a tower model; an electrical system model building module, used to perform modeling based on equipment parameters to obtain an electrical system model and control strategy; and a simulation model building module, used to build a model based on the wind speed model, blade model, tower model, electrical system model, and control strategy to obtain a digital twin model of the wind turbine.

[0069] Specifically, the wind speed model building module acquires data such as wind speed, wind direction, turbulence intensity, vertical wind shear, and horizontal wind shear using a nacelle-type laser wind radar, and then models the wind speed based on these data to obtain the wind speed model.

[0070] The blade model construction module acquires the blade's flapping direction stress, swing arm direction stress, strain, and bending moment based on the first load sensor. Using these parameters, and employing the blade element momentum method, the blade is divided into multiple aerodynamic characteristic regions for finite element analysis modeling to obtain the blade model. Specifically, the blade element momentum method involves dividing the blade into multiple aerodynamic characteristic regions for finite element analysis modeling to obtain the blade model. First, based on the blade's design parameters (such as blade length, shape, angle of attack distribution, etc.), the entire blade is divided into multiple small segments along its length, called "blade elements." For each blade element, the aerodynamic forces (lift and drag) at that location are calculated using Blade Element Momentum Theory (BEM). BEM assumes that each blade element operates independently and considers the effects of tip effect and induced velocity. By integrating the aerodynamic forces on all blade elements, the total aerodynamic forces and important parameters such as power output of the entire blade can be obtained. After obtaining the aerodynamic data provided by BEM, the next step is to build a three-dimensional model of the blade in the finite element analysis (FEA) software. This model should reflect the actual geometry and material properties of the blade as detailed as possible. The aerodynamic forces calculated by BEM are applied as external loads to the FEA model. This step requires converting the aerodynamic force distribution from the blade element distribution in the BEM to a load distribution suitable for the FEA (Finite Element Analysis) model. Static or dynamic analyses are performed to evaluate the blade's stress, strain, displacement, and other mechanical behaviors under different operating conditions. Furthermore, modal analysis can be performed to study the blade's vibration characteristics. Based on the analysis results, the blade design is optimized to ensure it meets the requirements for strength, stiffness, and fatigue life. The combination of BEM and FEA: BEM provides information on the blade's aerodynamics, while FEA focuses on the blade's structural response. Combining the two allows for a comprehensive and in-depth understanding of wind turbine blades. In practical applications, the aerodynamic force distribution of the blade under specific wind speed conditions is usually calculated using BEM first, and then these aerodynamic forces are imported as input loads into the FEA model for structural analysis. To improve analysis accuracy, iterative adjustments to parameters in the BEM model (such as angle of attack distribution) are sometimes necessary until the FEA results match experimental data or results from other advanced simulation tools.

[0071] The tower model construction module acquires the axial force along the tower centerline, the lateral force perpendicular to the tower centerline, the rotational torque acting on the tower, the bending moment acting on the tower section, and the tower vibration frequency and amplitude based on the second load sensor. Based on the axial force, lateral force, rotational torque, bending moment, vibration frequency and amplitude, and using the blade element dynamics method, the blades are divided into multiple aerodynamic characteristic regions for finite element analysis modeling to obtain the tower model.

[0072] The electrical system model building module uses Matlab or Simulink platform combined with FAST computing software to build electrical system models and control strategies for blade speed, generator speed, generator power, pitch angle, and yaw.

[0073] The simulation model building module combines wind speed, blade, tower, electrical system, and control strategies to create a digital twin simulation model of the wind turbine in Matlab or Simulink, thus obtaining the wind turbine digital twin model. This digital twin model is the initial model and requires data correction to obtain the final digital twin model (i.e., the target digital twin model). The specific process for data correction of the initial model is as follows:

[0074] The blade model and tower model are modified. An initial mechanism model of the wind turbine is established using the equipment attribute parameters provided by the wind turbine manufacturer. The output of the initial mechanism model is processed using the frequency response method. By comparing the theoretical response characteristics given by the wind turbine manufacturer with the model response characteristics, the parameters of the blade model and tower model are modified until the constraint conditions converge.

[0075] The control system structure and parameters are modified. Based on the modified model, the influence coefficient of environmental factors on the wind turbine is added, and a controller is formed to form a closed-loop control system for the wind turbine. Real-time acquired operating data is used as input to perform full-condition closed-loop simulation of the digital twin simulation model, obtaining the dynamic characteristics of blade speed, generator speed, generator power, pitch angle, and yaw. The dynamic characteristics are compared with the actual operating data and deviation analysis is performed. Based on the analysis results, the control structure and control parameters in the digital twin simulation model are iteratively adjusted to obtain the final digital twin simulation model.

[0076] In this embodiment, based on the existing wind turbine control system model, it is necessary to consider problems encountered in actual operation (such as insufficient performance and poor stability), as well as new requirements or goals (such as improving power generation efficiency and extending equipment life), and make appropriate adjustments to the structure and parameters of the control system. This step involves the selection of control algorithms, the design of controllers, and parameter tuning. Incorporating environmental factor influence coefficients: The operation of wind turbines is affected by various environmental factors, including wind speed, wind direction, temperature, and humidity. Therefore, based on the modified model, it is necessary to further consider the impact of these environmental factors on the wind turbine and quantify this impact as influence coefficients, adding them to the model. This makes the model closer to reality and improves its predictive accuracy. Adding a controller to form a closed-loop control system: After the above steps are completed, the designed controller is combined with the modified wind turbine model to form a closed-loop control system. Closed-loop control means that the system output is fed back to the input, and the control strategy is adjusted by comparing the difference between the expected value and the actual value, thereby achieving more precise control. Full-condition closed-loop simulation: Utilizing digital twin technology, this simulation uses real-time acquired operational data as input to perform a closed-loop simulation of the wind turbine under all operating conditions. "Full-condition" refers to all possible operating conditions the wind turbine might encounter, including varying wind speeds and directions. This method allows for a comprehensive evaluation of the wind turbine's performance under diverse conditions. Dynamic characteristic comparison and deviation analysis: The simulated dynamic characteristics, such as blade speed, generator speed, generator power, pitch angle, and yaw, are compared with actual operating data to analyze the deviations. Deviation analysis helps identify problems in the model, such as unreasonable model assumptions or inaccurate parameter settings. Iterative adjustment of control structure and parameters: Based on the results of the deviation analysis, the control structure and parameters in the digital twin simulation model are iteratively adjusted. This process may involve multiple iterations until the deviation between the model's predictions and the actual operating data reaches an acceptable range. Obtaining the final digital twin simulation model: After multiple rounds of iterative adjustments, when the model accurately reflects the actual operating conditions of the wind turbine, the final digital twin simulation model is considered obtained. This model can be used not only for the design optimization of wind turbine units, but also for fault diagnosis, performance prediction and many other aspects.

[0077] The key throughout the process lies in effectively combining theoretical knowledge with practical experience to continuously optimize the model, making it reflect the behavior of wind turbines as realistically as possible. Furthermore, advanced methods such as machine learning can be used to assist in the model's creation and optimization, further improving the accuracy and practicality of the digital twin model.

[0078] In some embodiments, the intelligent diagnostic module includes: an early warning model building module, which is used to build a model based on an intelligent sensing model to obtain an early warning model; a diagnostic model building module, which is used to build a model based on various sensing data, real-time operating data, equipment status data, and equipment failure mechanisms to obtain an equipment diagnostic model; and a fault early warning and diagnostic module, which is used to perform fault early warning diagnosis and life prediction based on the early warning model and / or the equipment diagnostic model.

[0079] Specifically, the early warning model construction module, based on intelligent sensing models and data analysis algorithms, enables early warning and accurate diagnosis of wind turbine faults. The system should be able to predict potential fault types and issue early warning signals based on the changing trends and characteristic information of monitored data. The diagnostic model construction module, through collected sensing data, real-time operating data, and equipment status data, constructs diagnostic models for specific equipment problems based on equipment failure mechanisms. The fault early warning and diagnosis module can quickly locate fault locations and analyze fault causes based on the early warning and / or diagnostic models. By learning and analyzing historical fault data, a fault diagnosis knowledge base is established to continuously improve the accuracy and efficiency of fault diagnosis. It can also provide detailed fault diagnosis reports, including fault descriptions, impact ranges, and recommended maintenance measures, providing strong technical support for maintenance personnel. The virtual sensing model construction module builds a data model based on the environment, operating conditions, and sensing loads, forming a standardized model for pilot turbine models. This model supports load inversion and load linkage for wind turbine clusters, enabling accurate simulation.

[0080] Furthermore, the digital twin intelligent sensing system applied to wind turbine units also includes a data management service unit applicable to the end-side, site-side, and central side. This data management service unit integrates IoT configuration, data acquisition, and data storage.

[0081] Optionally, in some embodiments, the data management service unit includes: an edge-side IoT layer; the edge-side IoT layer includes: a data processing module, which processes structured, semi-structured, or unstructured data collected from the edge (including but not limited to data cleaning, noise reduction, format conversion, removal of abnormal data and noise interference); a data encoding model construction module, which constructs a standardized data encoding module; a user permission configuration module, which supports permission configuration for users on both the site and center sides; and a data storage strategy configuration module, which supports data storage strategy configuration, including data storage duration, data storage structure, and data storage type under different management architectures.

[0082] Furthermore, this edge-side IoT layer also includes: a device model definition and mapping module, which is used to define custom digital representations of devices in the edge-side IoT layer and construct device models based on these digital representations, mapping the physical attributes and functions of the devices to the device models; a device model version update module, which is used to incrementally update the device models based on a preset update strategy after detecting a device model version update; a device model sharing and collaboration module, which is used to support the sharing and collaboration of device models; and a visualization module, which provides a graphical interface to support configuring and expanding new devices and defining device attributes and behaviors.

[0083] This invention constructs a data management service integrating IoT configuration, data acquisition, data storage, and data services based on the needs of sensing-type data management. It supports all software functions required by the edge-side IoT layer, including object model definition and mapping, IoT protocol configuration (supporting common industrial standardized protocols, including OPCUA, Modbus, MQTT, etc.), version management, and user management; it supports open-source IoT protocol management and configuration; the data management service supports data interface service encapsulation modes including RESTful APIs, and supports user interface encapsulation and publishing; it supports user applications on the site and center sides, providing convenient user permission configuration; it supports the deployment and upgrading of data encoding models, achieving standardized storage based on data encoding models before data acquisition and storage; it supports the configuration of data storage strategies, including configuration of data storage duration, data storage structure, and data storage type under different management architectures; it supports common data governance methods, providing configuration of common data cleaning, data preprocessing, data reduction, and data quality evaluation methods based on data characteristics, including diagnosis of data anomalies such as constant value data, abnormal data, out-of-limit data, and null value data, and quality evaluation methods such as data integrity rate and data availability rate. The data storage solution provides multimodal data storage formats based on the characteristics of perception-based data, supporting structured, semi-structured, and unstructured data storage. It also helps in selecting the right database for different types of data, forming a standardized data storage solution.

[0084] In this embodiment, a customized intelligent sensing system for offshore wind power, integrating edge computing, data management, and digital twin applications, is developed using standardized data acquisition, sensing, and IoT technologies. The system includes research on various advanced sensing technologies, scalable data acquisition technologies based on standardized data acquisition modules, and edge computing technologies. Furthermore, the system can perform multi-source data fusion and hierarchical data storage at the site, complete standardized data management and applications based on IoT data, and develop and deploy virtual sensing, intelligent diagnostics and early warning, and digital twin simulation models at the central site. These functions can be flexibly adjusted according to the system's deployment location.

[0085] refer to Figure 2 The present invention also provides a digital twin intelligent sensing method for wind turbine generators. This digital twin intelligent sensing method for wind turbine generators is applied to the aforementioned disclosed digital twin intelligent sensing system for wind turbine generators.

[0086] like Figure 2 As shown, in a preferred embodiment, the digital twin intelligent sensing method applied to wind turbine units includes the following steps:

[0087] Step S201: Obtain sensing data and device parameters from the edge.

[0088] Specifically, based on the sensing data obtained by the data sensing layer, blade parameters (such as the aforementioned blade flapping direction stress, swing arm direction stress, strain, bending moment, etc.), tower parameters (such as the aforementioned axial force, lateral force, rotational torque, bending moment, vibration frequency, and amplitude), wind-related parameters (such as the aforementioned wind speed, wind direction, turbulence intensity, vertical wind shear, and horizontal wind shear, etc.), and related equipment parameters (such as blade speed, generator speed, generator power, pitch angle, yaw, etc.) can be obtained.

[0089] Step S202: Perform finite element analysis modeling based on the sensing data to obtain the wind speed model, blade model, and tower model.

[0090] Step S203: Model based on equipment parameters to obtain electrical system model and control strategy.

[0091] Step S204: Combine the wind speed model, blade model, tower model, electrical system model, and control strategy to construct a model and obtain a digital twin model of the wind turbine.

[0092] Step S205: Correct the data of the digital twin model of the wind turbine to obtain the digital twin model of the target.

[0093] Step S206: Connect the target's digital twin model with the cloud platform.

[0094] Specifically, a cloud platform, server, operating system, and database are built, and the local IDE calls the communication space to achieve data exchange between the digital twin simulation model and the cloud platform.

[0095] Step S207: After data exchange, perform fault early warning diagnosis and life prediction based on the constructed equipment diagnostic model.

[0096] Specifically, after data interoperability, the cloud platform will be further developed to achieve functions such as operation status detection, visualization, fault early warning and diagnosis, and service life prediction.

[0097] Additionally, an electronic device of the present invention includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the digital twin intelligent sensing method for wind turbines as described above. Specifically, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, when the computer program is downloaded, installed, and executed by an electronic device, it performs the functions defined in the methods of the embodiments of the present invention. The electronic device of the present invention can be a terminal such as a laptop, desktop computer, tablet computer, or smartphone, or a similar computing device, or a server. Taking running on a mobile terminal as an example, such as... Figure 3 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 302 (which may include a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 304 for storing data are also shown. The mobile terminal may further include a transmission device 306 for communication functions and an input / output device 308. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0098] The memory 304 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to a data information security protection method in this embodiment of the invention. The processor 302 executes various functional applications and data processing by running the computer program stored in the memory 304, thereby implementing the aforementioned method. The memory 304 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 304 may further include memory remotely located relative to the processor 302, and these remote memories can be connected to the mobile terminal via a network. Examples of such networks include the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0099] The transmission device 306 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device 306 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 306 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0100] Furthermore, one type of storage medium of the present invention stores a computer program thereon, which, when executed by a processor, implements the digital twin intelligent sensing method for wind turbine generators described above. Specifically, it should be noted that the storage medium described above in the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0101] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0102] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0103] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0104] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0105] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They do not limit the scope of protection of the present invention. All equivalent changes and modifications made within the scope of the claims of the present invention should fall within the scope of the claims of the present invention.

Claims

1. A digital twin intelligent sensing system for wind turbine generators, characterized in that, include: End side, station side, and center side; The end-side includes: a data sensing layer and a data acquisition and calculation layer; the data sensing layer includes multiple sensing devices for monitoring and acquiring various sensing data; the data acquisition and calculation layer is used for acquiring, processing, calculating and storing raw data, as well as acquiring and processing the sensing data; the multiple sensing devices include: a first load sensor installed on the inner wall of the wind turbine blade, a second load sensor installed on the tower section, a nacelle-type laser wind radar installed on the top of the nacelle, a vibration pickup installed on the tower section, a dynamic tilt angle monitoring sensor installed on the top of the tower, and a blade acoustic signature monitoring device installed on the surface of the wind turbine blade; The data acquisition and computing layer includes: a standardized data acquisition module, an edge computing module, a data storage module, a controller, and a fieldbus for connecting the modules; the standardized data acquisition module is used to collect and process the various types of sensor data in a standardized manner; the data storage module is used to store data; the controller is used to control and manage the various modules; and the edge computing module is used to collect, calculate, and store the raw data. The station side includes: a zone 1 data management module, a zone 2 data management module, and a zone 3 data management application module; the zone 1 data management module is used to access and manage the sensing data; the zone 2 data management module is used to cache the sensing data and manage the sensing data through gates; the zone 3 data management application module is used to manage and apply the sensing data and the raw data. The central side includes: a digital twin simulation model construction module, a virtual sensing module, and an intelligent diagnosis module; the digital twin simulation model construction module is used to construct a digital twin model of the wind turbine using a combination of finite element analysis and data correction; the virtual sensing module is used to access the various sensing data, real-time operating data, and equipment status data; the intelligent diagnosis module is used to construct an equipment diagnosis model based on the various sensing data, the real-time operating data, the equipment status data, and the equipment failure mechanism, and to perform fault early warning diagnosis and life prediction based on the equipment diagnosis model; The digital twin simulation model construction module includes: A wind speed model building module is used to build a model based on wind-related parameters to obtain a wind speed model. A blade model construction module is used to perform finite element analysis modeling based on blade parameters to obtain a blade model. A tower model construction module is used to perform finite element analysis modeling based on tower parameters to obtain a tower model. An electrical system model building module is used to perform model building based on equipment parameters to obtain an electrical system model and control strategy. The simulation model construction module is used to construct a model based on the wind speed model, the blade model, the tower model, the electrical system model, and the control strategy to obtain a digital twin model of the wind turbine. The intelligent diagnostic module includes: An early warning model construction module is used to construct an early warning model based on an intelligent sensing model. A diagnostic model building module is used to build a model based on the various sensing data, the real-time operating data, the equipment status data, and the equipment failure mechanism to obtain the equipment diagnostic model. A fault early warning and diagnosis module is used to perform fault early warning diagnosis and life prediction based on the early warning model and / or the equipment diagnosis model.

2. The digital twin intelligent sensing system for wind turbine generators according to claim 1, characterized in that, The edge computing module includes: An edge node device, wherein the edge node device is used to collect the raw data; The data preprocessing module is used to filter, reduce noise, and remove outliers from the raw data, as well as to perform data format conversion and standardization. A local storage module, wherein the local storage module is used for local storage of the original data; An edge computing submodule is used to perform data analysis and computation based on the edge device. The communication module utilizes network slicing technology to provide connectivity between the edge node device and the cloud or other devices.

3. The digital twin intelligent sensing system for wind turbine generators according to claim 1 or 2, characterized in that, Also includes: A data management service unit applied to the terminal side, the station side, and the center side; The data management service unit includes: an edge-side IoT layer; The endpoint IoT layer includes: A data processing module is used to process the structured data, semi-structured data, or unstructured data collected from the terminal side. A data encoding model construction module, which is used to construct a standardized data encoding module; The user permission configuration module is used to support users on the site side and the center side to configure permissions. A data storage strategy configuration module is used to support data storage strategy configuration.

4. The digital twin intelligent sensing system for wind turbine generators according to claim 3, characterized in that, The endpoint IoT layer also includes: The object model definition and mapping module is used to define and map the devices in the edge IoT layer in a custom digital representation and construct an object model based on the digital representation, and map the physical attributes and functions of the devices to the object model. The object model version update module is used to incrementally update the object model based on a preset update strategy after detecting an object model version update. The object model sharing and collaboration module is used to support the sharing and collaboration of object models; A visualization module, which provides a graphical interface.

5. A digital twin intelligent sensing method for wind turbine generators, applied to the digital twin intelligent sensing system for wind turbine generators as described in any one of claims 1-4, characterized in that, Includes the following steps: Acquire sensing data and device parameters from the edge; Based on the sensing data, finite element analysis modeling is performed to obtain wind speed model, blade model and tower model; Based on the equipment parameters, a model is created to obtain an electrical system model and control strategy. By combining the wind speed model, the blade model, the tower model, the electrical system model, and the control strategy, a digital twin model of the wind turbine is obtained. The digital twin model of the wind turbine is corrected to obtain the digital twin model of the target. The digital twin model of the target is made compatible with the cloud platform for data exchange. After data exchange, fault early warning diagnosis and life prediction are carried out based on the constructed equipment diagnostic model.

6. A storage medium, characterized in that, The storage medium stores a computer program adapted for loading by a processor to execute the steps of the digital twin intelligent sensing method for wind turbines as described in claim 5.

7. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the digital twin intelligent sensing method for wind turbines as described in claim 5 by calling the computer program stored in the memory.

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