Method and related device for monitoring reliability of variable pitch of blade of wind turbine generator

Abstract

The invention discloses a method and a related device for monitoring the reliability of blade variable pitch of a wind turbine generator. The method comprises the following steps: detecting strain, temperature, crack and environmental data of a blade; estimating a health entropy value through a health entropy evaluation model according to the detected strain, temperature, crack and environment data of the blade; and evaluating the reliability of blade variable pitch of the wind turbine generator according to the healthy entropy. The method and the related device can accurately monitor the reliability of blade variable pitch of the wind turbine generator.

Description

Technical Field

[0001] This invention belongs to the field of wind turbine blade technology and relates to a reliability monitoring method and related device for wind turbine blade pitch control. Background Technology

[0002] In recent years, as the wind power industry has developed towards larger scale and deeper sea applications, the pitch system, as the core control mechanism of wind turbine generators, directly determines the power generation efficiency and operational safety of the generators.

[0003] Currently, the reliability of wind turbine pitch control systems is mainly achieved through field testing, laboratory simulation, hardware redundancy, and digital twin technology. However, field testing is limited by uncontrollable environments, high costs, and extreme conditions; simulation experiments struggle to accurately reproduce multi-physics coupling, resulting in significant errors. On the hardware side, redundant designs using identical configurations carry a risk of common-cause failure, with failure probabilities reaching as high as 22% under extreme conditions such as lightning strikes. Encoder redundancy fails to consider hub backlash, leading to a 40% decrease in reverse pitch control speed accuracy. Digital twin platforms are often based on single sensor data, making it difficult to integrate crack and environmental information, resulting in prediction errors exceeding 30%, reliance on passive maintenance, and high maintenance costs and low efficiency.

[0004] Overall, existing technologies have significant shortcomings in extreme condition simulation, redundancy design lacks fault tolerance, multi-source information fusion, and proactive prediction capabilities, which restrict the safety and efficient operation of wind turbine units. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and related device for monitoring the reliability of blade pitch of a wind turbine. This method and related device can accurately monitor the reliability of blade pitch of a wind turbine.

[0006] To achieve the above objectives, this invention discloses a method for reliability monitoring of blade pitch in wind turbines, comprising: Detect the strain, temperature, cracks, and environmental data of the blades; Based on the strain, temperature, crack and environmental data of the blade obtained from the detection, the health entropy value is estimated through the health entropy assessment model; The reliability of the blade pitch of the wind turbine is assessed based on the health entropy value.

[0007] A further improvement of the reliability monitoring method for blade pitch control of wind turbines described in this invention is as follows: Furthermore, it also includes: The strain, temperature, crack and environmental data of the blade are fused, and power optimization control and speed tolerance are performed.

[0008] Furthermore, the process of fusing the strain, temperature, crack, and environmental data of the blade is as follows: Calculate the optimal pitch angle ; Based on the optimal pitch angle An adaptive Kalman filter is used to fuse the strain, temperature, crack, and environmental data of the blade.

[0009] Furthermore, the process of assessing the reliability of the wind turbine blade pitch control based on the health entropy value is as follows: when When it is normal, the power output is normal; when it is normal, the power output is normal. If this occurs, the redundant unit will be activated and the pitch rate will be limited to 70%. At that time, a forced feathering shutdown is triggered, and life prediction is activated. This represents the health entropy value.

[0010] Furthermore, this also includes: making hardware switching and predictive maintenance decisions.

[0011] Furthermore, the process of executing hardware switching and predictive maintenance decisions is as follows: Switches between 400V / 690V system and rectification mode in case of power supply / driver failure; Predicting blade life; Calculate the maintenance priority for each device.

[0012] Furthermore, the predicted leaf lifespan for:

[0013] in, For strain sequence, It is a temperature sequence. This represents the amount of crack growth.

[0014] This invention discloses a reliability monitoring system for blade pitch control in wind turbine generators, comprising: A multi-source heterogeneous sensing layer is used to collect data on blade strain, temperature, cracks, and the environment. A digital twin hub is used for data fusion and to perform power optimization control and speed fault tolerance. Health entropy assessment model, used to calculate health entropy value. And trigger a tiered response; The dynamic fault tolerance strategy module is used to execute hardware switching and predictive maintenance decisions.

[0015] This invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a reliability monitoring method for blade pitch control of a wind turbine.

[0016] The present invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a reliability monitoring method for blade pitch control of a wind turbine.

[0017] The present invention has the following beneficial effects: The reliability monitoring method and related device for wind turbine blade pitch control described in this invention, in specific operation, estimates the health entropy value through a health entropy assessment model based on the detected blade strain, temperature, crack, and environmental data. The reliability of the wind turbine blade pitch control is then assessed based on the health entropy value. This addresses the problems of insufficient fault tolerance in redundant design, significant deficiencies in multi-source information fusion, and proactive prediction capabilities. It achieves the goal of accurately monitoring the reliability of wind turbine blade pitch control, enhances the system's proactive prediction capabilities, detects potential faults in advance, and reduces sudden accidents. Attached Figure Description

[0018] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0019] 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, not all, of the embodiments of the present invention. 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.

[0020] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0021] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0023] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0024] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0026] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0027] Example 1 refer to Figure 1 The reliability monitoring method for blade pitch control of wind turbines according to the present invention includes: Detect the strain, temperature, cracks, and environmental data of the blades; Based on the strain, temperature, crack and environmental data of the blade obtained from the detection, the health entropy value is estimated through the health entropy assessment model; The reliability of the blade pitch of the wind turbine is assessed based on the health entropy value.

[0028] This embodiment also includes: The strain, temperature, crack and environmental data of the blade are fused, and power optimization control and speed tolerance are performed.

[0029] In this embodiment, the process of fusing the strain, temperature, crack, and environmental data of the blade is as follows: Calculate the optimal pitch angle ; Based on the optimal pitch angle An adaptive Kalman filter is used to fuse the strain, temperature, crack, and environmental data of the blade.

[0030] In this embodiment, the process of assessing the reliability of the wind turbine blade pitch control based on the health entropy value is as follows: when When it is normal, the power output is normal; when it is normal, the power output is normal. If this occurs, the redundant unit will be activated and the pitch rate will be limited to 70%. At that time, a forced feathering shutdown is triggered, and life prediction is activated. This represents the health entropy value.

[0031] This embodiment also includes: performing hardware switching and predictive maintenance decisions.

[0032] In this embodiment, the process of executing hardware switching and predictive maintenance decisions is as follows: Switches between 400V / 690V system and rectification mode in case of power supply / driver failure; Predicting blade life; Calculate the maintenance priority for each device.

[0033] In this embodiment, the predicted blade lifespan is obtained. for:

[0034] in, For strain sequence, It is a temperature sequence. This represents the amount of crack growth.

[0035] Example 2 refer to Figure 1 The reliability monitoring system for blade pitch control of the wind turbine of the present invention includes: The multi-source heterogeneous sensing layer is used to collect data on the blade's strain, temperature, cracks, and environment. Specifically, the multi-source heterogeneous sensing layer includes a fiber Bragg grating sensor, an acoustic emission probe, an infrared thermal imager, a 3D digital imaging system, and a non-uniform execution unit. The fiber Bragg grating sensor monitors the blade strain distribution at a sampling rate of ≥10Hz. Acoustic emission probes utilize an event counting model. Detecting crack length The infrared thermal imager acquired the blade surface temperature at a resolution of 128×128. 3D digital imaging system tracks deformed point clouds The non-consistent execution unit is configured with a differentiated hardware matrix.

[0036] In this embodiment, the differentiated hardware matrix configured for the inconsistent execution unit is as follows: .

[0037] In this embodiment, the non-consistent execution unit includes a supercapacitor, a battery-powered backup power supply, and a 40kA / 50kA differentiated surge protection unit.

[0038] Digital twin hub: used for data fusion and to perform power optimization control and speed fault tolerance; The digital twin central control module includes a power optimization control module, an adaptive Kalman filter, and a redundant speed fault-tolerant module. The power optimization control module maximizes the power coefficient. The optimal blade pitch angle is calculated; an adaptive Kalman filter fuses multi-source data and updates the state vector; a redundant speed fault-tolerant module is used to switch blade encoder data when the motor encoder fails, and the blade speed is calculated through a backlash compensation algorithm.

[0039] In this embodiment, by maximizing the power coefficient Calculate the optimal pitch angle for:

[0040] in, The wind speed includes a turbulent component.

[0041] In this embodiment, the adaptive Kalman filter includes a state vector. State transition matrix A and observation matrix H.

[0042] In this embodiment, the state vector of the adaptive Kalman filter for:

[0043] in, The length of the crack. To achieve the optimal pitch angle, For the strain distribution of the blade, This refers to the surface temperature of the blade.

[0044] In this embodiment, the state transition matrix A of the adaptive Kalman filter is:

[0045] in, This is the aerodynamic damping coefficient. It is the fatigue attenuation factor. This is the temperature-crack coupling coefficient.

[0046] In this embodiment, the observation matrix H of the adaptive Kalman filter is:

[0047] The diagonal values ​​correspond to the confidence weights of the fiber optic grating, infrared thermal imager, and acoustic emission probe.

[0048] In this embodiment, the rotational speed calculated by the backlash compensation algorithm is... for:

[0049] in, This refers to the tooth clearance of the wheel hub bearing.

[0050] Health entropy assessment model: used to calculate health entropy values. And trigger a tiered response; In this embodiment, the health entropy value for:

[0051] in, For sensor or actuator weights, For the first The failure probability of each subsystem For the first The number of historical faults in each unit This represents the total system runtime.

[0052] The hierarchical response in this embodiment is specifically as follows: At that time, normal power output will occur. If this occurs, the redundant unit will be activated and the pitch rate will be limited to 70%. If this occurs, the feathering engine will be forced to stop and life prediction will be triggered.

[0053] Dynamic fault tolerance strategy module: used to execute hardware switching and predictive maintenance decisions; The dynamic fault-tolerant strategy module includes a fault-tolerant execution unit, an LSTM lifetime prediction unit, and a maintenance decision unit. The fault-tolerant execution unit is used to switch between a 400V / 690V system and rectification mode in the event of a power supply / driver failure. The LSTM lifetime prediction unit is used to predict the lifespan of a system based on a strain sequence. Temperature sequence and crack growth Predict blade life; the maintenance decision unit is used to calculate the maintenance priority of each piece of equipment.

[0054] In this embodiment, the blade lifetime predicted by the LSTM lifetime prediction unit is... for: .

[0055] In this embodiment, the specific calculation formula for the maintenance decision unit is as follows: .

[0056] This invention achieves multi-dimensional, real-time monitoring through a multi-source heterogeneous sensing layer, improving the comprehensiveness and accuracy of data and effectively coping with interference from complex environments. A digital twin hub integrates multi-source data, dynamically updating blade status and enhancing fault identification and response speed. A health entropy assessment model quantifies the system's health status, enabling tiered early warning and proactive prediction, which helps in the early detection of potential hazards. A dynamic fault-tolerance strategy ensures system robustness in the event of hardware failure, extending blade lifespan and reducing sudden failures through intelligent switching and lifespan prediction. This effectively solves the problems of incomplete data, untimely response, and fault handling in blade pitch monitoring, improving the safety of wind turbine operation.

[0057] Specifically, the present invention has the following characteristics: This invention achieves multi-dimensional, real-time monitoring through a multi-source heterogeneous sensing layer, improving the comprehensiveness and accuracy of data and effectively coping with interference from complex environments. A digital twin hub integrates multi-source data, dynamically updating blade status and enhancing fault identification and response speed. A health entropy assessment model quantifies the system's health status, enabling tiered early warning and proactive prediction, which helps in the early detection of potential hazards. A dynamic fault-tolerance strategy ensures system robustness in the event of hardware failure, extending blade lifespan and reducing sudden failures through intelligent switching and lifespan prediction. This effectively solves the problems of incomplete data, untimely response, and fault handling in blade pitch monitoring, improving the safety, reliability, and intelligence level of wind turbine operation.

[0058] This invention uses a multi-source heterogeneous sensing layer to collect blade strain, crack, temperature and environmental parameters in real time, ensuring the comprehensiveness and multi-dimensionality of the data. Furthermore, the fusion of multi-source data helps to more accurately reflect the true state of the blade. At the same time, the complementary characteristics of different sensors enhance the anti-interference ability and detection sensitivity of the monitoring, thereby improving the accuracy and timeliness of the monitoring.

[0059] This invention integrates multi-source data in real time through digital twins, dynamically updates the virtual model of the physical blade, and uses an adaptive Kalman filter for state estimation, which improves the accuracy of fault detection and reduces false and missed detections. At the same time, through the power optimization control module and the redundant speed fault-tolerant module, the pitch angle is optimized, improving the operating efficiency and reliability of the blade.

[0060] This invention quantifies the reliability of each subsystem of the system by using entropy values, which can comprehensively assess the health status of the blade pitch system and take different levels of measures based on the entropy response, thereby improving the flexibility and efficiency of responding to anomalies, enhancing the system's proactive prediction capabilities, detecting potential faults in advance, and reducing sudden accidents.

[0061] This invention enables proactive switching and fault tolerance under fault conditions, ensuring that the blade pitch control continues to operate even when some hardware fails, thus enhancing the robustness of the system. At the same time, by inputting sequential data such as strain, temperature, and crack growth, the LSTM model is used to predict lifespan, enabling automatic calculation of maintenance priorities and optimizing the allocation and scheduling of maintenance resources.

[0062] The module division in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in each embodiment of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0063] Example 3 A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a method for reliable monitoring of blade pitch of a wind turbine, including, for example: detecting blade strain, temperature, cracks, and environmental data; estimating a health entropy value using a health entropy assessment model based on the detected blade strain, temperature, cracks, and environmental data; and assessing the reliability of the wind turbine blade pitch based on the health entropy value. The memory may include main memory, such as high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which may be an industry-standard architecture bus, a peripheral component interconnection standard bus, an extended industry-standard architecture bus, etc., and the bus may be divided into address bus, data bus, control bus, etc. The memory stores the program; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0064] Example 4 A computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of a reliability monitoring method for blade pitch control of a wind turbine. For example, the method includes: detecting blade strain, temperature, cracks, and environmental data; estimating a health entropy value using a health entropy assessment model based on the detected blade strain, temperature, cracks, and environmental data; and assessing the reliability of the wind turbine blade pitch control based on the health entropy value. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0065] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0066] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0067] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0068] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0069] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0070] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

[0071] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for reliability monitoring of blade pitch control in wind turbine units, characterized in that, include: Detect the strain, temperature, cracks, and environmental data of the blades; Based on the strain, temperature, crack and environmental data of the blade obtained from the detection, the health entropy value is estimated through the health entropy assessment model; The reliability of the blade pitch of the wind turbine is assessed based on the health entropy value.

2. The reliability monitoring method for blade pitch control of a wind turbine according to claim 1, characterized in that, Also includes: The strain, temperature, crack and environmental data of the blade are fused, and power optimization control and speed tolerance are performed.

3. The method for reliability monitoring of blade pitch control in wind turbine units according to claim 2, characterized in that, The process of fusing the strain, temperature, crack, and environmental data of the blade is as follows: Calculate the optimal pitch angle ; Based on the optimal pitch angle An adaptive Kalman filter is used to fuse the strain, temperature, crack, and environmental data of the blade.

4. The reliability monitoring method for blade pitch control of a wind turbine according to claim 1, characterized in that, The process of assessing the reliability of wind turbine blade pitch control based on the health entropy value is as follows: when When it is normal, the power output is normal; when it is normal, the power output is normal. If this occurs, the redundant unit will be activated and the pitch rate will be limited to 70%. At that time, a forced feathering shutdown is triggered, and life prediction is activated. This represents the health entropy value.

5. The method for reliability monitoring of blade pitch control in wind turbine units according to claim 1, characterized in that, Also includes: Perform hardware switching and predictive maintenance decisions.

6. The method for reliability monitoring of blade pitch control in wind turbine units according to claim 5, characterized in that, The process of making hardware switching and predictive maintenance decisions is as follows: Switches between 400V / 690V system and rectification mode in case of power supply / driver failure; Predicting blade life; Calculate the maintenance priority for each device.

7. The reliability monitoring method for blade pitch control of a wind turbine according to claim 6, characterized in that, Predicted leaf life for: in, For strain sequence, It is a temperature sequence. This represents the amount of crack growth.

8. A reliability monitoring system for blade pitch control of a wind turbine, characterized in that, include: A multi-source heterogeneous sensing layer is used to collect data on blade strain, temperature, cracks, and the environment. A digital twin hub is used for data fusion and to perform power optimization control and speed fault tolerance. Health entropy assessment model, used to calculate health entropy value. And trigger a tiered response; The dynamic fault tolerance strategy module is used to execute hardware switching and predictive maintenance decisions.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the reliability monitoring method for blade pitch control of the wind turbine as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the reliability monitoring method for blade pitch control of the wind turbine as described in any one of claims 1-7.

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